Methods for generating consensus biomass estimates

ABSTRACT

Generating consensus biomass estimates include providing a first biomass parameter data set associated with a first biomass attribute parameter to a first biomass estimation model and providing a second biomass parameter data set associated with a second biomass attribute parameter to a second biomass estimation model different from the first biomass estimation model. The first biomass estimation model is adaptively weighted with a first weighting factor relative to a second weighting factor for the second biomass estimation model. An aggregated biomass estimate is determined based on a combination of the first biomass estimation model using the first weight factor and the second biomass estimation model using the second weight factor.

BACKGROUND

Husbandry, such as in agriculture and aquaculture, includes raisinganimals for their meat, fiber, milk, eggs, or other products. Monitoringof animal growth parameters in a frequent and quantitative manner isuseful for assisting in the maintenance of animal health and to maximizeproduction efficiency. For example, in livestock farming, regularmonitoring of animal weight provides an indication of animal growthrates. The live weight of animals is of particular interest, as itserves as an index for animal growth, health, and readiness for market.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure may be better understood, and its numerousfeatures and advantages made apparent to those skilled in the art byreferencing the accompanying drawings. The use of the same referencesymbols in different drawings indicates similar or identical items.

FIG. 1 is a diagram illustrating a system for implementing consensusbiomass estimation in accordance with some embodiments.

FIG. 2 is a diagram illustrating a biomass estimation systemimplementing two sets of different underwater sensors in accordance withsome embodiments.

FIG. 3 is a diagram illustrating a first example of adaptive weightingof biomass estimation models in accordance with some embodiments.

FIG. 4 is a flow diagram of a method for providing a biomass estimate inaccordance with some embodiments.

FIG. 5 is a diagram illustrating a biomass estimation systemimplementing two sets of image-based sensors in accordance with someembodiments.

FIG. 6 is a diagram illustrating a terrestrial biomass estimation systemimplementing various sensor systems in accordance with some embodiments.

FIG. 7 is a block diagram illustrating a system configured to provide aconsensus biomass estimate in accordance with some embodiments.

FIG. 8 is a block diagram illustrating a second example of adaptiveweighting of biomass estimates in accordance with some embodiments

FIG. 9 is a block diagram illustrating an example of adaptive weightingof biomass attribute parameters for multi-parameter biomass estimationmodels in accordance with some embodiments.

DETAILED DESCRIPTION

In animal husbandry, farmers theoretically aim for the highest growthrate possible, using the least amount of feed to produce the bestquality output. Typically, aquaculture refers to the cultivation offish, shellfish, aquatic plants, and the like through husbandry effortsfor seafood markets and human consumption. Feed conversion ratio (FCR)is a ratio or rate measuring the efficiency with which the bodies ofanimals convert animal feed into a desired output. With dairy cows, forexample, the desired output is milk, whereas in animals raised for meat(e.g., beef cows, pigs, chickens, fish, shrimp, shellfish, and the like)the desired output is flesh/meat. In particular, the feed conversionratio is the ratio of inputs to outputs (e.g., mass of feed provided perbody mass gained by an animal). In some industries, the efficiency ofconverting feed into the desired output is represented as feedefficiency (FE), which is the output divided by the input (i.e., inverseof FCR).

Effective farm management benefits from accurate information regardinganimal biomass in order to monitor feed conversion, control feedingregimes, stocking densities, and optimum timing for harvesting of animalstock. Conventionally, animal farming companies employ variousstrategies for animal biomass determination, such as by manuallyweighing reared animals using mechanical or electronic scales. Suchmanual practices are labor-intensive, time-consuming, and oftendetrimental to welfare (e.g., stress inducing) of animals due tophysical handling of animals. Additionally, manual weight determinationtechniques are often too slow for high-throughput farming operationswhere many measurements are required, where stocking densities aresufficiently high, and/or for weighing animals such as cows and horseswhere sheer body mass renders manual measurements difficult. Inparticular, direct physical measurement of animals is often difficultsuch as due to its size scale, quantity of measurements to be taken, andthe like. Farming operations increasingly deploy non-contact,sensor-based biomass determination systems for performing analyses onsensor data (e.g., images and the like) related to animal measurements(such as body measurements extracted from image data) and predicting ananimal's weight.

However, the performance of such non-contact biomass estimation bysensor systems will have various inherent uncertainties or inaccuraciesin their performance due to, for example, the nature of the environmentsin which they are deployed. Existing image analysis systems haveinsufficient weight prediction accuracy, image capture reliability, andfeature extraction reliability, particularly in farming orlivestock-handling environments where sensor data capture conditions arevariable (such as non-uniform lighting). For example, aquaculture stockis, with few exceptions, often held underwater and therefore moredifficult to observe than animals and plants cultured on land. Further,aquaculture is commonly practiced in open, outdoor environments andtherefore exposes farmed animals, farm staff, and farming equipment tofactors that are, at least partially, beyond the control of operators.Such factors include, for example, variable and severe weatherconditions, changes to water conditions, turbidity, interference withfarm operations from predators, and the like.

To improve the precision and accuracy of biomass estimation and decreaseuncertainties associated with conventional biomass measurement systems,FIGS. 1-9 describe techniques for providing a consensus biomassestimation that includes providing a first biomass parameter data setassociated with a first biomass attribute parameter to a first biomassestimation model and providing a second biomass parameter data setassociated with a second biomass attribute parameter is provided to asecond biomass estimation model different from the first biomassestimation model. The first biomass estimation model is adaptivelyweighted with a first weighting factor relative to a second weightingfactor for the second biomass estimation model. An aggregated biomassestimate is determined based on a combination of the first biomassestimation model using the first weight factor and the second biomassestimation model using the second weight factor. In this manner,different biomass estimations from different biomass estimation modelsare adaptively weighted and combined into an aggregated biomass estimatethat is more accurate than would be individually provided by eachbiomass estimation model by itself.

FIG. 1 is a diagram of a system 100 for implementing biomass estimationin accordance with some embodiments. In various embodiments, the system100 includes a plurality of sensor systems 102 that are each configuredto monitor and generate data associated with the environment 104 withinwhich they are placed. As shown, the plurality of sensor systems 102includes a first sensor system 102 a positioned below the water surface106 and including a first set of one or more sensors. The first set ofone or more sensors are configured to monitor the environment 104 belowthe water surface 106 and generate data associated with a first biomassattribute parameter. It will be appreciated that biomass attributeparameters, in various embodiments, include one or more parameterscorresponding to the environment 104 within which the one or moresensors are positioned and may be measured (or otherwise captured anddetected) to generate parameter data sets 108 to be used in biomassestimation models. Accordingly, in some embodiments, the first sensorsystem 102 a generates a first parameter data set 108 a and communicatesthe first parameter data set 108 a to a processing system 110 forstorage, processing, and the like.

As used herein, the term “biomass” refers to any representation (e.g.,including both quantitative and qualitative) or other description of aparameter (which may be based on sensor measurements, derived fromsensor measurements, and the like) for describing an amount of animalmass in a given area, biotic community, habitat, population, or sample.It should be recognized that measures of biomass are not limited to anyparticular expression or dimension and may include any representationincluding but not limited to weight of an individual animal, mean weightof animals in a population, median weight of animals in a population,weight distribution of animals in a population, cumulative total weightof animals per unit area/volume, number of animals per unit area/volume,body size of an animal, body size average of animals in a population,body size variation of animals in a population, body size distribution,body shape, vertical distribution of animal densities (e.g., fish withina water column), vertical animal size stratification, horizontaldistribution of animal densities (e.g., flock of chickens on barnfloor), and the like.

Similarly, as used herein, the term “biomass attribute” refers to anyrepresentation (e.g., including both quantitative and qualitative) orother description of a parameter (which may be based on sensormeasurements, derived from sensor measurements, input based on humanobservations, and the like) from which the biomass of one or moreanimals (or one or more body parts/sections of individual animals) maybe estimated or is otherwise indicative of biomass. It should berecognized that biomass attributes are not limited to any particularexpression and may include any number and any combination of parametricrepresentations including but not limited to distance (e.g., length)between key morphological features of an animal (e.g., transect lines inlateral views, posterior views, anterior views, and the like), variousbody circumferential measurements, various body curvature measurements,various body volumetric measurements, body composition measurements,various morphological parameters, and the like.

For example, in some embodiments, biomass attributes in the context offinfish include, but is not limited to, total length (e.g., length of afish as measured from tip of the snout to tip of the longer lobe ofcaudal fin, often expressed as a straight-line measure and not over thecurve of the body), standard length (e.g., length of a fish as measuredfrom tip of the snout to the posterior end of the last vertebra, therebyexcluding length of the caudal fin), fork length (e.g., length of a fishas measured from tip of the snout to the end of the middle caudal finrays), one or more transects of a truss network between keymorphological features of a fish (e.g., lateral distance between acenter of an eye and the original of the dorsal fin), body depth alongone or more portions of a fish (e.g., lateral distance between dorsaland ventral body portions, such as lateral distance of fish body at endof dorsal fin), body girth around one or more portions of a fish (e.g.,contouring measurement around a circumference of the body, such asaround a fattest portion, a thinnest portion, and around the fish at oneor more body depth transect lines), and the like.

Although described above in the context of image-related biomassattributes for ease of description, those skilled in the art willrecognize that biomass attributes are not limited to such contexts andincludes any measurement contribution from a physical setting from whichbiomass may be estimated or is otherwise indicative of biomass. Invarious embodiments, biomass attributes in the context of finfish alsoinclude, but is not limited to, sound pulse reflections, returningacoustic echo signals, background noise data, water attenuation data,acoustic target strength of one or more animals, volume-backscatteringstrength from a population of animals, acoustic scattering data,acoustic data across various frequencies, transect pattern dataregarding animal data capture, and the like.

In some embodiments, biomass attributes in the context of livestockanimals include, but is not limited to, body contour measurements, pointcloud data (PCD), hip height, withers height, hips distance, head size,body girth around one or more portions of a fish (e.g., contouringmeasurement around a circumference of the body, such as around thechest, around the heart, and around the animal at one or more body depthtransect lines), body depth along one or more portions of an animal(e.g., chest depth), chest width (e.g., body depth along frontal view),abdominal width, rump width, one or more transects of a truss networkbetween key morphological features in one or more views, body length(e.g., distance between base of ears and to the base of the tail), depthdata corresponding to animal positions, water displacement, and thelike.

Those skilled in the art will recognize that this discussion of examplerepresentations of biomass (e.g., different representations of how toquantify and present biomass information) and biomass attributes isprovided only for illustrative purposes to give a concrete example ofthe weighting and biomass estimation operations discussed herein.However, any of a variety of biomass representations and/or biomassattributes may be implemented for generating consensus biomassestimations, as described in more detail below.

The plurality of sensor systems 102 also includes a second sensor system102 b positioned below the water surface 106 and including a second setof one or more sensors. The second set of one or more sensors areconfigured to monitor the environment 104 below the water surface 106and generate data associated with a second biomass attribute parameter.It will be appreciated that biomass attribute parameters, in variousembodiments, include one or more parameters corresponding to theenvironment 104 within which the one or more sensors are positioned andmay be measured (or otherwise captured and detected) to generateparameter data sets 108 to be used in biomass estimation models.Accordingly, in some embodiments, the second sensor system 102 bgenerates a second parameter data set 108 b and communicates the secondparameter data set 108 b to the processing system 110 for storage,processing, and the like. It should be recognized that the plurality ofsensor systems 102 are not limited to being positioned under the watersurface 106. In some embodiments, the plurality of sensor systems 102also includes a third sensor system 102 c positioned at or above thewater surface 106 and including a third set of one or more sensors. Thethird set of one or more sensors are configured to monitor theenvironment 104 at or above the water surface 106 and generate dataassociated with a third biomass attribute parameter. Accordingly, insome embodiments, the third sensor system 102 c generates a thirdparameter data set 108 c and communicates the third parameter data set108 c to the processing system 110 for storage, processing, and thelike.

The processing system 110 includes one or more processors 112 coupledwith a communications bus 114 for processing information. In variousembodiments, the one or more processors 112 include, for example, one ormore general purpose microprocessors or other hardware processors. Theprocessing system 110 also includes one or more storage devices 116communicably coupled to the communications bus 114 for storinginformation and instructions. For example, in some embodiments, the oneor more storage devices 116 includes a magnetic disk, optical disk, orUSB thumb drive, and the like for storing information and instructions.In various embodiments, the one or more storage devices 116 alsoincludes a main memory, such as a random-access memory (RAM), cacheand/or other dynamic storage devices, coupled to the communications bus114 for storing information and instructions to be executed by the oneor more processors 112. The main memory may be used for storingtemporary variables or other intermediate information during executionof instructions to be executed by the one or more processors 112. Suchinstructions, when stored in storage media accessible by the one or moreprocessors 112, render the processing system 110 into a special-purposemachine that is customized to perform the operations specified in theinstructions. By way of non-limiting example, in various embodiments,the processing system 110 may be any computer system, such as aworkstation, desktop computer, server, laptop, handheld computer, tabletcomputer, mobile computing or communication device, or other form ofcomputing or telecommunications device that is capable of communicationand that has sufficient processor power and memory capacity to performthe operations described herein.

The processing system 110 also includes a communications interface 118communicably coupled to the communications bus 114. The communicationsinterface 118 provides a multi-way data communication couplingconfigured to send and receive electrical, electromagnetic or opticalsignals that carry digital data streams representing various types ofinformation. In various embodiments, the communications interface 118provides data communication to other data devices via, for example, anetwork 120. For example, in some embodiments, the processing system 110may be configured to communicate with one or more remote platforms 122according to a client/server architecture, a peer-to-peer architecture,and/or other architectures via a network 116. Remote platform(s) 122 maybe configured to communicate with other remote platforms via theprocessing system 110 and/or according to a client/server architecture,a peer-to-peer architecture, and/or other architectures via the network116. Users may access system 100 via remote platform(s) 122.

A given remote platform 122 may include one or more processorsconfigured to execute computer program modules. The computer programmodules may be configured to enable a user associated with the givenremote platform 122 to interface with system 100 and/or externalresources 124, and/or provide other functionality attributed herein toremote platform(s) 122. External resources 122 may include sources ofinformation outside of system 100, external entities participating withsystem 100, and/or other resources. In some implementations, some or allof the functionality attributed herein to external resources 122 may beprovided by resources included in system 100.

In some embodiments, the processing system 110, remote platform(s) 122,and/or one or more external resources 124 may be operatively linked viaone or more electronic communication links. For example, such electroniccommunication links may be established, at least in part, via thenetwork 120. It will be appreciated that this is not intended to belimiting, and that the scope of this disclosure includes implementationsin which computing platform(s) 102, remote platform(s) 104, and/orexternal resources 122 may be operatively linked via some othercommunication media. Further, in various embodiments, the processingsystem 110 is configured to send messages and receive data, includingprogram code, through the network 120, a network link (not shown), andthe communications interface 118. For example, a server 126 may beconfigured to transmit or receive a requested code for an applicationprogram through via the network 120, with the received code beingexecuted by the one or more processors 112 as it is received, and/orstored in storage device 116 (or other non-volatile storage) for laterexecution.

In various embodiments, the processing system 110 receives one or moreof the parameter data sets 108 and stores the parameter data sets 108 atthe storage device 116 for processing. As described in more detail belowwith respect to FIGS. 2-9, the processing system 110 provides a firstbiomass attribute parameter data set (e.g., first parameter data set 108a) to a first biomass estimation model and further provides a secondbiomass attribute parameter data set (e.g., second parameter data set108 b) to a second biomass estimation model different from the firstbiomass estimation model. In various embodiments, the first biomassestimation model receives a data set corresponding to measurements forat least a first biomass attribute parameter related to biomassestimation. Similarly, the second biomass estimation model receives adata set corresponding to measurements for at least a second biomassattribute parameter related to biomass estimation. By way ofnon-limiting example, in some embodiments, a biomass attribute parameterdescribes body length measurements of fish within the water below thewater surface 106.

Data corresponding to such a biomass attribute parameter may be utilizedas input by a biomass estimation model to generate a description of oneor more biomass-related metrics (i.e., a biomass estimation).Subsequently, the processing system 110 adaptively weights the firstbiomass estimation model with a first weighting factor relative to asecond weighting factor for the second biomass estimation model in orderto determine an aggregated biomass estimate based on a combination ofthe first biomass estimation model using the first weight factor and thesecond biomass estimation model using the second weight factor. In thismanner, the processing system 110 provides a weighting to differentbiomass estimates from different biomass estimation models and combinesthem into an aggregated biomass estimate that is more accurate thanwould be individually provided by each biomass estimation model byitself.

As will be appreciated by those skilled in the art, sensor systemscapturing data for biomass estimation also experience various inherentuncertainties or inaccuracies in their performance similar to physicallyhandling a subset of an animal population due to, for example, thenature of the environments in which they are deployed. In the context ofunderwater finfish, sampling a number of individuals that is less thanthe entirety of an entire population is likely to result in biasedsampling. For example, when sampling a population for estimating abiomass metric (e.g., estimating a trait such as average size ofindividual fish in the whole population), biased sampling may occur dueto chance effects where small and/or large individuals areoverrepresented by chance. Similarly, animals of different sizes withinthe population size distribution may not be equally distributed withinthe rearing enclosure, thereby oversampling individuals positionedcloser to sensor systems and undersampling individuals positioned awayor imperceptible by sensor systems (as many sensors such as imagecameras do not see the entirety of the rearing enclosures). These typesof sampling errors result in decreased accuracy and precision of biomassestimates, particularly with populations with increasedwithin-population variations and with smaller sampling sizes.

As described in more detail below with respect to FIGS. 2-9, byassigning weight factors to the biomass estimation models and theirrespective biomass estimations, the system influences the biomassattribute parameters utilized and therefore combine the biomassattribute parameters in an advantageous way. The resulting aggregatedbiomass estimate therefore indicates for multiple biomass attributeparameters and biomass estimation models and may have an improvedbiomass estimation accuracy. For example, using multiple biomassestimations from multiple sources and combining them in an optimal waybased on a validation and comparison of model parameters with referencedata (e.g., weather data, weather forecasts, environmental conditions,and more as discussed in further detail below) provides a biomassestimate with an increased accuracy by accounting for relative relevanceof available data under various conditions.

In various embodiments, the system 100 also includes a feed controlsystem (not shown) that receives a feed instruction signal (such asfeeding instruction signals based on biomass triggered instructionsignals 310 and 810 described in more detail below with respect to FIGS.3 and 8) for modifying and/or guiding the operations (e.g., dispensingof feed related to meal size, feed distribution, meal frequency, feedrate, feed composition, etc.) of feeding systems including automaticfeeders, feed cannons, and the like. It should be recognized that thefeed instruction signal is not limited to any particular format and mayinclude any representation including but not limited to feedinginstructions, user interface alerts, and the like. As will beappreciated, in various embodiments, users and operators may be capableof modifying their feeding strategy manually based on the feedinstruction signal. For example, in some embodiments, the operator mayprovide fewer feed pellets when the feed instruction signal provides asignal that individual and/or cumulative population biomass is low, suchas via graphical user interface (GUI) displays, audible and visualalerts, and the like. In other embodiments, the feed instruction signalmay be provided to an automatic feeder for controlling feedingoperations in a manner that reduces or eliminates manual, humanintervention. Accordingly, various parameters such as feeding rate,feeding amount, feeding frequency, feeding timing, and the like may bemodified based on the feeding instruction signal.

Referring now to FIG. 2, illustrated is a diagram showing a system 200implementing two sets of different underwater sensors in accordance withsome embodiments. In various embodiments, the system 200 includes aplurality of sensor systems 202 that are each configured to monitor andgenerate data associated with the environment 204 within which they areplaced. As shown, the plurality of sensor systems 202 includes a firstsensor system 202 a positioned below the water surface 206 and includinga first set of one or more sensors. The first set of one or more sensorsare configured to monitor the environment 204 below the water surface206 and generate data associated with a first biomass attributeparameter. In particular, the first sensor system 202 a of FIG. 2includes one or more acoustic sensors configured to observe fishbehavior and capture measurements associated with biomass attributeparameters. For example, in various embodiments, the acoustic sensorsare configured to capture acoustic data corresponding to the presence(or absence), abundance, distribution, size, and behavior of underwaterobjects (e.g., a population of fish 212 as illustrated in FIG. 2).

Such acoustic data measurements may, for example, measure intensity ofacoustic reflectivity of ensonified objects(s) (e.g., fish) within thewater to be used as an approximation of biomass. As used herein, itshould be appreciated that an “object” refers to any stationary,semi-stationary, or moving object, item, area, or environment in whichit may be desired for the various sensor systems described herein toacquire or otherwise capture data of. For example, an object mayinclude, but is not limited to, one or more fish, crustacean, feedpellets, predatory animals, and the like. However, it should beappreciated that the sensor measurement acquisition and analysis systemsdisclosed herein may acquire and/or analyze sensor data regarding anydesired or suitable “object” in accordance with operations of thesystems as disclosed herein.

In various embodiments, the one or more acoustic sensors of the firstsensor system 202 a includes one or more of a passive acoustic sensorand/or an active acoustic sensor (e.g., an echo sounder and the like)for remotely detecting and identifying objects. Passive acoustic sensorsgenerally listen for remotely generated sounds (e.g., often at specifiedfrequencies or for purposes of specific analyses, such as for detectingfish or feed in various aquatic environments) without transmitting intothe underwater environment 204. In various embodiments, many species offishes and other animals produce sounds naturally in various conditions,with the produced sounds indicative of various fish behaviors, fishabundance, and also other biomass attributes. It should be noted thatthe sounds of many species are not produced continuously but rather atspecific times, such as more commonly at night or during periods ofspecific behavioral activities such as feeding. Active acoustic sensorsare conventionally include both an acoustic receiver and an acoustictransmitter that transmit pulses of sound (e.g., pings) at one or morefrequencies into the surrounding environment 204 and then listens forreflections (e.g., echoes) of the sound pulses for remotely detectingtargets.

It is noted that as sound waves/pulses travel through water, it willencounter objects having differing densities or acoustic properties thanthe surrounding medium (i.e., the underwater environment 204) thatreflect sound back towards the active sound source(s) utilized in activeacoustic systems. For example, sound travels differently through fish212 (and other objects in the water such as feed pellets 214) thanthrough water (e.g., a fish's air-filled swim bladder has a differentdensity than water). Accordingly, differences in reflected sound wavesfrom active acoustic techniques due to differing object densities may beaccounted for in the detection of aquatic life and estimation of theirindividual sizes or total biomass. It should be recognized that althoughspecific sensors are described below for illustrative purposes, variousacoustic sensors may be implemented in the systems described hereinwithout departing from the scope of this disclosure.

In various embodiments, the first sensor system 202 a utilizes activesonar systems in which pulses of sound are generated using a sonarprojector including a signal generator, electro-acoustic transducer orarray, and the like. The active sonar system may further include abeamformer (not shown) to concentrate the sound pulses into an acousticbeam 216 covering a certain search angle 218. In some embodiments, thefirst sensor system 202 a measures distance through water between twosonar transducers or a combination of a hydrophone (e.g., underwateracoustic microphone) and projector (e.g., underwater acoustic speaker).The first sensor system 202 a includes sonar transducers (not shown) fortransmitting and receiving acoustic signals (e.g., pings). To measuredistance, one transducer (or projector) transmits an interrogationsignal and measures the time between this transmission and the receiptof a reply signal from the other transducer (or hydrophone). The timedifference, scaled by the speed of sound through water and divided bytwo, is the distance between the two platforms. This technique, whenused with multiple transducers, hydrophones, and/or projectorscalculates the relative positions of objects in the underwaterenvironment 204.

In other embodiments, the first sensor system 202 a includes an acoustictransducer configured to emit sound pulses into the surrounding watermedium. Upon encountering objects that are of differing densities thanthe surrounding water medium (e.g., the fish 212), those objects reflectback a portion of the sound (sometimes referred to as the echo signal)towards the sound source (i.e., the acoustic transducer). Due toacoustic beam patterns, identical targets at different azimuth angleswill return different echo levels. Accordingly, if the beam pattern andangle to a target is known, this directivity may be compensated for. Invarious embodiments, split-beam echosounders divide transducer facesinto multiple quadrants and allow for location of targets in threedimensions. Similarly, multi-beam sonar projects a fan-shaped set ofsound beams outward from the first sensor system 202 a and record echoesin each beam, thereby adding extra dimensions relative to the narrowerwater column profile given by an echosounder. Multiple pings may thus becombined to give a three-dimensional representation of objectdistribution within the water environment 204.

In some embodiments, the one or more acoustic sensors of the firstsensor system 202 a includes a Doppler system using a combination ofcameras and utilizing the Doppler effect to monitor the biomass ofsalmon in sea pens. The Doppler system is located underwater andincorporates a camera, which is positioned facing upwards towards thewater surface 206. In various embodiments, there is a further cameraintegrated in the transmission device normally positioned on thetop-right of the pen, monitoring the surface of the pen. The sensoritself uses the Doppler effect to differentiate pellets from fish. Thesensor produces an acoustic signal and receives the echo. The sensor istuned to recognize pellets and is capable of transmitting theinformation by radio link to the feed controller over extendeddistances. The user watches the monitor and determines when feedingshould be stopped. Alternatively, a threshold level of waste pellets canbe set by the operator, and the feeder will automatically switch offafter the threshold level is exceeded.

In other embodiments, the one or more acoustic sensors of the firstsensor system 202 a includes an acoustic camera having a microphonearray (or similar transducer array) from which acoustic signals aresimultaneously collected (or collected with known relative time delaysto be able to use phase different between signals at the differentmicrophones or transducers) and processed to form a representation ofthe location of the sound sources. In various embodiments, the acousticcamera also optionally includes an optical camera. It will beappreciated that various characteristics of echo signals (or otherreturn signal to the active acoustic receiver) strength include biomassattributes that are correlated to, for example, fish count, totalbiomass, and the like.

Additionally, as illustrated in FIG. 2, the plurality of sensor systems202 includes a second sensor system 202 b positioned below the watersurface 206 and including a second set of one or more sensors. Thesecond set of one or more sensors are configured to monitor theenvironment 204 below the water surface 206 and generate data associatedwith a second biomass attribute parameter. In particular, the secondsensor system 202 b of FIG. 2 includes one or more imaging sensorsconfigured to observe fish behavior and capture measurements associatedwith biomass attribute parameters related to fish biomass. In variousembodiments, the imaging sensors are configured to capture image datacorresponding to, for example, the presence (or absence), abundance,distribution, size, and behavior of underwater objects (e.g., apopulation of fish 212 as illustrated in FIG. 2). In variousembodiments, such captured image data is processed to extract variousmeasurements corresponding to biomass attributes (e.g., variousimage-related biomass attributes such as previously described withrespect to FIG. 1). It should be recognized that although specificsensors are described below for illustrative purposes, various imagingsensors may be implemented in the systems described herein withoutdeparting from the scope of this disclosure.

In some embodiments, the imaging sensors of the second sensor system 202b includes one or more cameras configured to capture still images and/orrecord moving images (e.g., video data). The one or more cameras aredirected towards the surrounding environment 204 below the water surface206, with each camera capturing a sequence of images (e.g., videoframes) of the environment 204 and any objects in the environment. Invarious embodiments, each camera has a different viewpoint or pose(i.e., location and orientation) with respect to the environment.Although FIG. 2 only shows a single camera for ease of illustration anddescription, persons of ordinary skill in the art having benefit of thepresent disclosure should appreciate that the second sensor system 202 bcan include any number of cameras and which may account for parameterssuch as each camera's horizontal field of view, vertical field of view,and the like. Further, persons of ordinary skill in the art havingbenefit of the present disclosure should appreciate that the secondsensor system 202 b can include any arrangement of cameras (e.g.,cameras positioned on different planes relative to each other,single-plane arrangements, spherical configurations, and the like).

In various embodiments, the imaging sensors of the second sensor system202 b includes a first camera (or lens) having a particular field ofview 220 as represented by the dashed lines that define the outer edgesof the camera's field of view that images the environment 204 or atleast a portion thereof. For the sake of clarity, only the field of view220 for a single camera is illustrated in FIG. 2. In variousembodiments, the imaging sensors of the second sensor system 202 bincludes at least a second camera having a different but overlappingfield of view (not shown) relative to the first camera (or lens). Imagesfrom the two cameras therefore form a stereoscopic pair for providing astereoscopic view of objects in the overlapping field of view. Further,it should be recognized that the overlapping field of view is notrestricted to being shared between only two cameras. For example, atleast a portion of the field of view 220 of the first camera of thesecond sensor system 202 b may, in some embodiments, overlap with thefields of view of two other cameras to form an overlapping field of viewwith three different perspectives of the environment 204.

In some embodiments, the imaging sensors of the second sensor system 202b includes one or more light field cameras configured to capture lightfield data emanating from the surrounding environment 204. In otherwords, the one or more light field cameras captures data not only withrespect to the intensity of light in a scene (e.g., the light fieldcamera's field of view/perspective of the environment) but also thedirections of light rays traveling in space. In contrast, conventionalcameras generally record only light intensity data. In otherembodiments, the imaging sensors of the second sensor system 202 bincludes one or more range imaging cameras (e.g., time-of-flight andLIDAR cameras) configured to determine distances between the camera andthe subject for each pixel of captured images. For example, such rangeimaging cameras may include an illumination unit (e.g., some artificiallight source) to illuminate the scene and an image sensor with eachpixel measuring the amount of time light has taken to travel from theillumination unit to objects in the scene and then back to the imagesensor of the range imaging camera.

It should be noted that the various operations are described here in thecontext of multi-camera or multi-lens cameras. However, it should berecognized that the operations described herein may similarly beimplemented with any type of imaging sensor without departing from thescope of this disclosure. For example, in various embodiments, theimaging sensors of the second sensor system 202 may include, but are notlimited to, any of a number of types of optical cameras (e.g., RGB andinfrared), thermal cameras, range- and distance-finding cameras (e.g.,based on acoustics, laser, radar, and the like), stereo cameras,structured light cameras, ToF cameras, CCD-based cameras, CMOS-basedcameras, machine vision systems, light curtains, multi- andhyper-spectral cameras, thermal cameras, other machine vision systemsthat are operable along various portions of the electromagnetic spectrum(including visible light, infrared, and other bands), sonar cameras(e.g., DIDSON and ARTS sonar cameras), and the like.

Additionally, as illustrated in FIG. 2, the plurality of sensor systems202 includes a third sensor system 202 c positioned below the watersurface 206 and including a third set of one or more sensors. The thirdset of one or more sensors are configured to monitor the environment 204below the water surface 206 and generate data associated with areference parameter. In particular, the third sensor system 202 c ofFIG. 2 includes one or more environmental sensors configured to capturemeasurements associated with the environment 204 within which the system200 is deployed. As described in further detail below, in variousembodiments, the environmental sensors of the third sensor system 202 cgenerate environmental data that serves as reference data forimplementing the dynamic weighting of various estimates from a pluralityof biomass estimation models.

For example, in one embodiment, the environmental sensors of the thirdsensor system 202 c includes a turbidity sensor configured to measure anamount of light scattered by suspended solids in the water. Turbidity isa measure of the degree to which water (or other liquids) changes inlevel of its transparency due to the presence of suspended particulates(e.g., by measuring an amount of light transmitted through the water).In general, the more total suspended particulates or solids in water,the higher the turbidity and therefore murkier the water appears. Aswill be appreciated, a variable parameter such as variance in theturbidity of a liquid medium will affect the accuracy of image-basedmeasurements and accordingly the accuracy of prediction models thatconsume such images as input. Accordingly, by measuring turbidity as areference parameter, the detected intensity of turbidity measurementsmay be utilized as a basis for determining a weighting that animage-based prediction model should be given relative to other non-imagebased prediction models, as described in more detail below. It should berecognized that although FIG. 2 is described in the specific context ofa turbidity sensor, the third sensor system 202 c may include any numberof and any combination of various environmental sensors withoutdeparting from the scope of this disclosure.

The first sensor system 202 a and the second sensor system 202 b eachgenerate a first biomass attribute parameter data set 208 a and a secondbiomass attribute parameter data set 208 b, respectively. In the contextof FIG. 2, the first biomass attribute parameter includes acoustic data.Such acoustic data may include any acoustics-related value or othermeasurable factor/characteristic that is representative of at least aportion of a data set that describes the presence (or absence),abundance, distribution, size, and/or behavior of underwater objects(e.g., a population of fish 212 as illustrated in FIG. 2). For example,the acoustic data may include acoustic measurements indicative of therelative and/or absolute locations of individual fish of the populationof fish 212 within the environment 204. Similarly, the acoustic data mayinclude acoustic measurements corresponding to target strength of fishand indicative of biomass.

In some embodiments, the acoustic data includes biomass data forestimating individual fish sizes based on measured target strengthusing, for example, backscattering cross section of individual fish, orother target strength estimation methodologies. This allows estimationof fish sizes using fish target strength versus length relationships.Similarly, in various embodiments, acoustic data also include biomassdata for estimating total fish biomass based on measurements of fishdensity and density distribution using various echo integrationmethodologies, such as estimating extinction cross section of fishmeasured from attenuation of sound waves by fish aggregation. It shouldbe recognized that although the first biomass attribute parameter hasbeen abstracted and described here generally as “acoustic data” for easeof description, those skilled in the art will understand that acousticdata (and therefore the first biomass attribute parameter data set 208 acorresponding to the acoustic data) may include, but is not limited to,any of a plurality of acoustics measurements corresponding to one ormore biomass attributes, acoustic sensor specifications, operationalparameters of acoustic sensors, and the like.

In the context of FIG. 2, the second biomass attribute parameterincludes image data. Such image data may include any image-related valueor other measurable factor/characteristic that is representative of atleast a portion of a data set that describes the presence (or absence),abundance, distribution, size, and/or behavior of underwater objects(e.g., a population of fish 212 as illustrated in FIG. 2). For example,the image data may include camera images capturing data related to oneor more biomass attributes of individual fish of the population of fish212 within the environment 204.

It should be recognized that although the second biomass attributeparameter has been abstracted and described here generally as “imagedata” for ease of description, those skilled in the art will understandthat image data (and therefore the second biomass attribute parameterdata set 208 b corresponding to the image data) may include, but is notlimited to, any of a plurality of image frames corresponding to one ormore biomass attributes, extrinsic parameters defining the location andorientation of the image sensors (such as relative to the imagedobjects, other sensors such as the first sensor system 202 a, and thelike), intrinsic parameters that allow a mapping between cameracoordinates and pixel coordinates in an image frame, camera models,operational parameters of the image sensors (e.g., shutter speed), depthmaps, and the like.

In various embodiments, such as in the context of FIG. 2, the referenceparameter includes environmental data. Such environmental data mayinclude any measurement representative of the environment 204 withinwhich the environmental sensors are deployed. For example, theenvironmental data (and therefore the reference parameter data set 208 ccorresponding to the environmental data) may include, but is not limitedto, any of a plurality of water turbidity measurements, watertemperature measurements, metocean measurements, weather forecasts, airtemperature, dissolved oxygen, current direction, current speeds, andthe like.

In various embodiments, the processing system 210 receives one or moreof the data sets 208 (e.g., first biomass attribute parameter data set208 a, the second biomass attribute parameter data set 208 b, and thereference parameter data set 208 c) via, for example, wired-telemetry,wireless-telemetry, or any other communications links for processing.The processing system 210 provides the first biomass attribute parameterdata set 208 a to a first biomass estimation model 222 a. The processingsystem 210 also provides the second biomass attribute parameter data set208 b to a second biomass estimation model 222 b different from thefirst biomass estimation model 222 a. In various embodiments, the firstbiomass estimation model 222 a receives the acoustic data of the firstbiomass attribute parameter data set 208 a as input and generates afirst biomass estimate 224 a.

By way of non-limiting example, in some embodiments, the first biomassestimation model 222 a utilizes acoustic data related to target strengthof ensonified fish within the water below the water surface 206 as aproxy for biomass (as biomass is a value which is difficult to directlymeasure without physically handling the fish) in generating the firstbiomass estimate 224 a. In various embodiments, such as described belowin more detail with respect to FIG. 3, the first biomass estimate 224 agenerated by the first biomass estimation model 222 a is represented asa numerical weight within a numerical system. However, those skilled inthe art will recognize that such a numerical representation of biomassand biomass estimation is provided as a non-limiting example for ease ofillustration. As used herein, the term “biomass estimation” refers toany representation (e.g., including both quantitative and qualitative)or other description of a parameter (which may be based on sensormeasurements, derived from sensor measurements, input based on humanobservations, and the like).

Additionally, it should be recognized that a “biomass estimate” is notlimited to the output of biomass models and in some embodiments mayinclude, for example, human-based inputs such as textual entries in aspreadsheet indicating size or lifecycle status (e.g., fry vs. smolt vs.adult), numerical descriptors of fish size (e.g., biomass ranking on a0-10 scale based on human personal experience such as biomass estimation802 b of FIG. 8), and the like. In other embodiments, such as describedbelow in more detail with respect to FIG. 8, a “biomass estimate” mayalso include (model-based-output or otherwise) a raw numericalquantification without any relation to a baseline reference (e.g.,biomass estimation 802 a), a color-coded descriptor (e.g., biomassestimation 802 c), a percentage quantification of total biomass thathave reached a threshold size for harvesting (e.g., biomass exceeding 4kilograms per individual and therefore ready to be harvested, such asbiomass estimation 802 d), instructions to change feeding rate or totalamount (e.g., biomass estimation 802 e), and the like.

Further, it should be noted that although the various operations areprimarily described here in the context of estimating biomass for acurrent time period (e.g., in real-time or near real-time), theoperations described herein may similarly be applied to biomassestimation or description for a prior time period or a future timeperiod without departing from the scope of this disclosure. Accordingly,as used herein, a “forecast” or “prediction” (e.g., in the context ofbiomass) refers to describing, either qualitatively or quantitatively,any proxy related to estimated level of biomass.

The processing system 210 also provides the second biomass attributeparameter data set 208 b to a second biomass estimation model 222 b. Invarious embodiments, the second biomass estimation model 222 b receivesthe image data of the second biomass attribute parameter data set 208 bas input and generates a second biomass estimate 224 b. By way ofnon-limiting example, in some embodiments, the second biomass estimationmodel 222 b utilizes image data containing measurements related to oneor more biomass attributes of individual fish (or with respect to two ormore fish) for generating the second biomass estimate 224 b. In variousembodiments, the second biomass estimate 224 b is a numericaldescription of a body weight of an individual fish. In some embodiments,the second biomass estimate 224 b is a numerical description of anaverage body weight for fish within the population of fish 212.

Subsequently, the processing system 210 adaptively weights the firstbiomass estimate 224 a of the first biomass estimation model 222 a witha first weighting factor relative to a second weighting factor for asecond biomass estimate 224 b of the second biomass estimation model 222b in order to determine an aggregated biomass estimate 226 based on acombination of the first biomass estimation model 222 a using the firstweight factor and the second biomass estimation model 222 b using thesecond weight factor. In this manner, and as described in more detailbelow, the processing system 210 provides a weighting to differentbiomass estimates from different biomass estimation models and combinesthem into an aggregated biomass estimate 226 that is more accurate thanwould be individually provided by each biomass estimation model byitself.

In one embodiment, with respect to FIG. 3 and with continued referenceto FIG. 2, a plurality of biomass estimation models (e.g., the firstbiomass estimation model 222 a and the second biomass estimation model222 b) receive their respective inputs (e.g., the biomass attributeparameter data sets 208 a and 208 b) and generate a plurality of biomassestimates for a number N of different biomass estimation models (notshown) under a first set of environmental conditions in which theweather is sunny, waters are clean, and waves are choppy. As illustratedin FIG. 3, a first biomass estimation model using acoustic data as input(e.g., first biomass estimation model 222 a of FIG. 2) generates a firstbiomass estimation (e.g., first biomass estimate 224 a), as representedby a first biomass estimate 302 a in FIG. 3. A second biomass estimationmodel using image data as input (e.g., second biomass estimation model222 b of FIG. 2) generates a second biomass estimation (e.g., secondbiomass estimate 224 b), as represented by a second biomass estimate 302b in FIG. 3.

As illustrated, the first model biomass estimate 302 a is 4.5 kgsaverage fish weight on an example scale and representation of biomass asan average per individual live-body weight of the population of fish 212in kilogram (kg) units of mass in the metric system. The second modelbiomass estimate 302 b is 11.03 lbs average fish weight on an examplescale and representation of biomass as an average per individuallive-body weight of the population of fish 212 in pound (lb) units ofmass in the imperial system.

As previously discussed in more detail with respect to FIG. 2, theenvironmental sensors of the third sensor system 202 c generateenvironmental data that serves as reference data for implementing thedynamic weighting of various estimates from a plurality of biomassestimation models. Although the first model biomass estimate 302 a andthe second model biomass estimate 302 b both generally indicate asimilar level of estimated biomass, the two biomass estimates arerepresented in differing unit scales (e.g., metric kilograms versusimperial pounds). Accordingly, in various embodiments, the processingsystem 210 optionally (as indicated by the dotted lines) normalizes eachof the model biomass estimates 302 a through 302N to a common biomassrepresentation scale.

For example, as illustrated in FIG. 3, the processing system 210normalizes the model biomass based on a metric kilogram representationwith one decimal place after the decimal point to generate a firstnormalized model biomass estimate 304 a of 4.5 kgs associated with thefirst model biomass estimate 302 a of 4.5 kgs. Similarly, the processingsystem 210 normalizes the second model biomass estimate 302 b of 11.03lbs based on the same kilogram scale to generate a second normalizedmodel biomass estimate 304 b of 5.0 kg. Based on a comparison of thefirst and second biomass attribute parameter data sets 208 a, 208 brelative to the measured reference (e.g., environmental) data such asthe reference parameter data set 208 c, the processing system 210assigns a first weighting factor w₁ of 0.4 to the first biomassestimation model (e.g., the first biomass estimation model 222 a of FIG.2) and its associated first model biomass estimate 302 a and firstnormalized model biomass estimate 304 a. Additionally, in this examplewhere N=2 for using two different models in estimating biomass, theprocessing system 210 also assigns a second weighting factor w₂ of 0.6to the second biomass estimation model (e.g., the second biomassestimation model 222 b of FIG. 2) and its associated second modelbiomass estimate 302 b and second normalized model biomass estimate 304b.

The processing system 210 assigns this relative weighting with the firstweighting factor w₁ of 0.4 for the first biomass estimation model (basedon acoustic data) and the second weighting factor w₂ of 0.6 for thesecond biomass estimation model (based on image data) due to a first setof environmental conditions (e.g., using environmental data from theenvironmental sensors to measure conditions for a current time or toforecast for a future time period) in which the weather is sunny, watersare clean, but waves are choppy. At a high level of abstraction, theprocessing system 210 determines that the image data captured by thesecond sensor system 202 b (which is positively influenced by, forexample, ambient light due to the sunny conditions and clear waters, andalso relatively better per animal capture of biomass attributeinformation) will be of a relatively better quality than the acousticdata captured by the first sensor system 202 a (which is negativelyinfluenced by, for example, background sounds due to the choppy waveswhich decrease the signal-to-noise ratio of acoustic data).Subsequently, the processing system 210 applies the assigned weightingfactors w₁, w₂ to the first normalized model biomass estimate 304 a andthe second normalized model biomass estimate 304 b, respectively, togenerate a first weighted model biomass estimate 306 a of 1.8 kg and asecond weighted model biomass estimate 306 b of 3.0. Further, theprocessing system 210 combines these two weighted model biomassestimates 306 a, 306 b to generate a weighted, aggregated biomassestimate 308 of 4.8 kg and thereby integrates data from multi-sensorsystems to provide a biomass estimate.

In various embodiments, the systems described herein may optionallygenerate a biomass triggered instruction signal 310 (as represented bythe dotted line box) based at least in part on the aggregated biomassestimate 308 that instructs a user and/or one or more farm systemsregarding specific actions to be taken in accordance to the biomassestimate (e.g., as quantified by the aggregated biomass estimate 308).For example, in some embodiments, the biomass triggered instructionsignal 310 (as represented by the dotted line box) based at least inpart on the aggregated biomass estimate 308 that instructs an automatedfeeding system regarding specific actions to be taken in accordance tothe biomass estimate (e.g., change an amount of feed dispensed, change aformulation of feed dispensed, and the like).

It will be appreciated that the biomass triggered instruction signal 310is not limited to any particular format and in various embodiments maybe converted to any appropriate format to be compatible for intendedusage, including control signals for modifying operations of feedingsystems and display commands for presenting visual directions. Suchformats for the biomass triggered instruction signal 310 include, by wayof non-limiting example, a stop signal, a color-coded user interfacedisplay, a specific feed rate that should be administered, a total feedvolume that should be administered, and the like. Additionally, althoughdescribed above in the context of feeding-related signals, those skilledin the art will recognize that biomass triggered instruction signals arenot limited to such contexts and includes any response that may beinitiated based at least in part on biomass information such as feedoptimization (e.g., changing from a starter feed to a finishing feed,changing feeding rates), harvest actions (e.g., scheduling harvest orpickup of a portion or an entirety of an animal population), animaltransfer actions (e.g., transferring from one environment or enclosureto another, sorting animals into different enclosures, culling a subsetof the animal population), intervention actions (e.g., schedulingveterinarian visit due to unexpected stall or drop in growth rates), andthe like.

As will be appreciated, environmental conditions often vary and therelative accuracy of data gathered by different sensor systems will alsovary over time. In another embodiment, with respect to FIG. 3 and withcontinued reference to FIG. 2, a plurality of biomass estimation models(e.g., the first biomass estimation model 222 a and the second biomassestimation model 222 b) receive their respective inputs (e.g., thebiomass attribute parameter data sets 208 a and 208 b) and generate aplurality of biomass estimations for a number N of different biomassestimation models (not shown) under a second set of environmentalconditions in which the weather is dark (e.g., at dusk or dawn), watershave increased turbidity levels, but waves are calm. As illustrated inFIG. 3, a first biomass estimation model using acoustic data as input(e.g., first biomass estimation model 222 a of FIG. 2) generates a firstbiomass estimate (e.g., first biomass estimate 224 a), as represented bya first model biomass estimate 312 a in FIG. 3. A second biomassestimation model using image data as input (e.g., second biomassestimation model 222 b of FIG. 2) generates a second biomass estimate(e.g., second biomass estimate 224 b), as represented by a second modelbiomass estimate 312 b.

As illustrated, the first model biomass estimate 312 a is 4.5 kgsaverage fish weight on an example scale and representation of biomass asan average per individual live-body weight of the population of fish 212in kilogram (kg) units of mass in the metric system. The second modelbiomass estimate 312 b is 11.03 lbs average fish weight on an examplescale and representation of biomass as an average per individuallive-body weight of the population of fish 212 in pound (lb) units ofmass in the imperial system.

As previously discussed in more detail with respect to FIG. 2, theenvironmental sensors of the third sensor system 202 c generateenvironmental data that serves as reference data for implementing thedynamic weighting of various estimates from a plurality of biomassestimation models. Although the first model biomass estimate 312 a andthe second model biomass estimate 312 b both generally indicate asimilar level of estimated biomass, the two biomass estimates arerepresented in differing unit scales (e.g., metric kilograms versusimperial pounds). Accordingly, in various embodiments, the processingsystem 210 optionally (as indicated by the dotted lines) normalizes eachof the model biomass estimates 312 a through 312N to a common biomassrepresentation scale.

For example, as illustrated in FIG. 3, the processing system 210normalizes the model biomass based on a metric kilogram representationwith one decimal place after the decimal point to generate a firstnormalized model biomass estimate 314 a of 4.5 kgs associated with thefirst model biomass estimate 312 a of 4.5 kgs. Similarly, the processingsystem 210 normalizes the second model biomass estimate 312 b of 11.03lbs based on the same kilogram scale to generate a second normalizedmodel biomass estimate 314 b of 5.0 kg. As previously discussed in moredetail with respect to FIG. 2, the environmental sensors of the thirdsensor system 202 c generate environmental data that serves as referencedata for implementing the dynamic weighting of various estimates from aplurality of biomass estimation models.

In contrast to the first set of environmental conditions previouslydescribed, the second set of environmental conditions describes darkweather (e.g., at dusk or dawn), waters have increased turbidity levels,but waves are calm. Accordingly, based on a comparison of the first andsecond biomass attribute parameter data sets 208 a, 208 b relative tothe measured reference (e.g., environmental) data such as the referenceparameter data set 208 c, the processing system 210 assigns a firstweighting factor w₁ of 0.9 to the first biomass estimation model (e.g.,the first biomass estimation model 222 a of FIG. 2) and its associatedfirst model biomass estimate 312 a and first normalized model biomassestimate 314 a. Additionally, in this example where N=2 for using twodifferent models in estimating biomass, the processing system 210 alsoassigns a second weighting factor w₂ of 0.1 to the second biomassestimation model (e.g., the second biomass estimation model 222 b ofFIG. 2) and its associated second model biomass estimate 312 b andsecond normalized model biomass estimate 314 b.

At a high level of abstraction, the processing system 210 determinesthat the acoustic data captured by the first sensor system 202 a (whichis positively influenced by, for example, calm waters) will haveimproved quality relative to acoustic data captured by the same firstsensor system 202 a under less favorable conditions (e.g., the first setof environmental conditions previously described). Additionally, theprocessing system 210 determines that the image data captured by thesecond sensor system 202 b (which is negatively influenced by, forexample, dark ambient light conditions and turbid waters) will havedegraded quality (such as due to inaccuracies arising from visibilityissues) relative to image data captured by the same second sensor system202 b under more favorable conditions (e.g., the first set ofenvironmental conditions previously described).

Accordingly, the processing system 210 assigns a relative weighting withthe first weighting factor w₁ of 0.9 for the first biomass estimationmodel (based on acoustic data) and the second weighting factor w₂ of 0.1for the second biomass estimation model (based on image data) due to thesecond set of environmental conditions and discounts the image-based,second biomass estimation model that is expected to be less accurate inmurky waters. Subsequently, the processing system 210 applies theassigned weighting factors w₁, w₂ to the first normalized model biomassestimate 314 a and the second normalized model biomass estimate 314 b,respectively, to generate a first weighted model biomass estimate 316 aof 4.1 kgs and a second weighted model biomass estimate 316 b of 0.5kgs. Further, the processing system 210 combines these two weightedmodel biomass estimates 316 a, 316 b to generate a weighted, aggregatedbiomass estimate 318 of 4.6 kgs and thereby integrates data frommulti-sensor systems (some sensors and estimation models being moreaccurate than others, sometimes in all instances or sometimes dependingon variable factors such as the environment) measuring fish biomassattributes to provide a biomass estimate.

In various embodiments, the systems described herein may optionallygenerate a biomass triggered instruction signal 320 (as represented bythe dotted line box) based at least in part on the aggregated biomassestimate 318 that instructs a user and/or one or more farm systemsregarding specific actions to be taken in accordance to the biomassestimate (e.g., as quantified by the aggregated biomass estimate 318).For example, in some embodiments, the biomass triggered instructionsignal 320 (as represented by the dotted line box) based at least inpart on the aggregated biomass estimate 318 that instructs an automatedfeeding system regarding specific actions to be taken in accordance tothe biomass estimate (e.g., change an amount of feed dispensed, change aformulation of feed dispensed, and the like).

Referring now to FIG. 4, illustrated is a flow diagram of a method 400for providing a biomass estimate in accordance with some embodiments.For ease of illustration and description, the method 400 is describedbelow with reference to and in an example context of the systems 100 and200 of FIG. 1 and FIG. 2, respectively. However, the method 400 is notlimited to this example context, but instead may be employed for any ofa variety of possible system configurations using the guidelinesprovided herein.

The method 400 begins at block 402 with the receipt by a first biomassestimation model 222 a of a first biomass attribute parameter data set208 a associated with a first biomass attribute parameter. In variousembodiments, the operations of block 402 include providing, by aprocessing system, the first biomass attribute parameter data set 208 avia a wireless or wired communications link to the first biomassestimation model 222 a for processing. For example, in the context ofFIGS. 1 and 2, the sensor systems 102, 202 communicate at least thefirst parameter data set 108 a, 208 a to a processing system 110, 210for storage at a local storage device 116. As illustrated in FIG. 2, thefirst biomass estimation model 222 a is executed locally using the sameprocessing system 210 at which the first parameter data set 208 a isstored. Accordingly, the first parameter data set 208 a may be soprovided to the first biomass estimation model 222 a by transmitting oneor more data structures to processors 112 via a wireless or wired link(e.g., communications bus 114) for processing. It should be noted thatthe first parameter data set and the first biomass estimation model donot need to be stored and/or processed at the same device or system.Accordingly, in various embodiments, the providing of the firstparameter data set and its receipt by the first biomass estimation modelfor the operations of block 402 may be implemented in any distributedcomputing configuration (e.g., such as amongst the processing system110, network 120, remote platforms 122, external resources 124, andserver 126 of FIG. 1).

In at least one embodiment, the first parameter data set 108 a, 208 aincludes data corresponding to measurements for at least a first biomassattribute parameter related to biomass estimation. For example, withreference to FIG. 2, the first biomass attribute parameter includesacoustic data corresponding to the presence (or absence), abundance,distribution, size, and behavior of underwater objects (e.g., apopulation of fish 212 as illustrated in FIG. 2). Such acoustic datameasurements may therefore measure intensity of acoustic reflectivity ofensonified target(s) (e.g., fish) within the water to be used as anapproximation of biomass. Although described here in the context ofacoustic data characterizing physical properties of the population offish 212, acoustic data related to physical properties of otherunderwater objects such as feed 214 may also be measured for the firstparameter data set 208 a.

Additionally, in various embodiments, acoustic data related to behaviorof underwater objects may also be measured for the first parameter dataset 208 a. For example, in some embodiments, an acoustic sensor of thefirst sensor system 202 a may monitor noises generated by the populationof fish 212 during feeding (e.g., chomping noises resulting from jawmovement while the fish eat) as biomass attribute indicators. Similarly,in the context of swimming behavior, an acoustic sensor of the firstsensor system 202 may monitor movement noises generated by thepopulation of fish during feeding (e.g., noises resulting from swimmingmotion towards or away from feed pellets 214) such that an increasenoise may be indicative of increased fish biomass and the soundsgenerated within the water as they swim and displace water.

The method 400 continues at block 404 with the receipt by a secondbiomass estimation model 222 b (that is different from the first biomassestimation model) of a second biomass attribute parameter data set 208 bassociated with a second biomass attribute parameter. In variousembodiments, the operations of block 404 include providing, by aprocessing system, the second biomass attribute parameter data set 208 bvia a wireless or wired communications link to the second biomassestimation model 222 b for processing. For example, in the context ofFIGS. 1 and 2, the sensor systems 102, 202 communicate at least thesecond parameter data set 108 b, 208 b to a processing system 110, 210for storage at a local storage device 116. As illustrated in FIG. 2, thesecond biomass estimation model 222 b is executed locally using the sameprocessing system 210 at which the second parameter data set 208 b isstored. Accordingly, the second parameter data set 208 b may be soprovided to the second biomass estimation model 222 b by transmittingone or more data structures to processors 112 via a wireless or wiredlink (e.g., communications bus 114) for processing. It should be notedthat the second parameter data set and the second biomass estimationmodel do not need to be stored and/or processed at the same device orsystem. Accordingly, in various embodiments, the providing of the secondparameter data set and its receipt by the second biomass estimationmodel for the operations of block 404 may be implemented in anydistributed computing configuration (e.g., such as amongst theprocessing system 110, network 120, remote platforms 122, externalresources 124, and server 126 of FIG. 1).

In at least one embodiment, the second parameter data set 108 b, 208 bincludes data corresponding to measurements for at least a secondbiomass attribute parameter related to biomass estimation. For example,with reference to FIG. 2, the second biomass attribute parameterincludes image data corresponding to the presence (or absence),abundance, distribution, size, and behavior of underwater objects (e.g.,a population of fish 212 as illustrated in FIG. 2). In variousembodiments, such captured image data is processed to extract variousmeasurements corresponding to biomass attributes (e.g., variousimage-related biomass attributes such as previously described withrespect to FIG. 1). For example, such image data may be analyzed usingvarious image analysis techniques to identify various physicalproperties associated with the population of fish 212 such as fishpositions within the water, depth within the water, fish dimensionmeasurements, individual identification of one or more animals, biomasslocation within the water, orientation of one or more fish relative toother sensors, and the like to be used as biomass attributes. Further,image data related to properties of other underwater objects may also bemeasured for the second parameter data set 208 b.

It should be recognized that although biomass estimation was previouslydescribed with respect to FIGS. 1-4 in the context underwater acousticsensors, underwater image sensors, and underwater environmental sensors,data may be collected by any of a variety of imaging and non-imagingsensors. By way of non-limiting examples, in various embodiments, thesensor systems may include various sensors local to the site at whichthe fish are located (e.g., underwater telemetry devices and sensors),sensors remote to the fish site (e.g., satellite-based weather sensorssuch as scanning radiometers), various environmental monitoring sensors,active sensors (e.g., active sonar), passive sensors (e.g., passiveacoustic microphone arrays), echo sounders, photo-sensors, ambient lightdetectors, accelerometers for measuring wave properties, salinitysensors, thermal sensors, infrared sensors, chemical detectors,temperature gauges, or any other sensor configured to measure data thatwould have an influence on biomass estimation. It should be recognizedthat, in various embodiments, the sensor systems utilized herein are notlimited to underwater sensors and may include combinations of aplurality of sensors at different locations, such as illustrated anddescribed below with respect to FIG. 5. Additionally, it should also berecognized that, in various embodiments, the sensor systems utilizedherein are not limited to sensors of differing types. For example, invarious embodiments, the sensor systems may include two differentimage-data based sensor systems positioned at different locations (e.g.,under water and above water as illustrated and described below withrespect to FIG. 5) and/or a plurality of differing reference sensors.

The operations of method 400 continues at block 406 with adaptivelyweighting the first biomass estimation model with a first weightingfactor relative to a second weighting factor for the second biomassestimation model. The operations of block 406 includes providingmeasured reference data related to the first biomass attribute parameterand the second biomass attribute parameter. In various embodiments,providing measured reference data includes the third sensor system 202 cgenerating environmental data and providing the reference parameter dataset 208 c via a wireless or wired communications link to the processingsystem 210 for local storage and processing. It should be noted that thereference parameter data set 208 c does not need to be stored at thesame device or system at which the reference parameter data set 208 c isprocessed to determine weighting factors. Accordingly, in variousembodiments, the providing of the reference parameter data set 208 c andits receipt by the processing system 210 for the operations of block 406may be implemented in any distributed computing configuration (e.g.,such as amongst the processing system 110, network 120, remote platforms122, external resources 124, and server 126 of FIG. 1).

In at least one embodiment, the reference parameter data set 208 cincludes measured environmental reference data that is relevant to theprecision/accuracy of individual biomass estimation models, the relativeprecision/accuracy between different biomass estimation models, relativeavailability or reliability of data captured by any of the sensorsystems discussed herein, relative data granularity of data captured byany of the sensor systems discussed herein, and the like. Accordingly,in various embodiments, the processing system 210 assigns, based on acomparison of the biomass estimation models with the measured referencedata, a first weight factor to the first biomass estimation model and asecond weight factor to the second biomass estimation model.

With reference to FIGS. 2-3, the third sensor system 202 c of FIG. 2includes one or more environmental sensors configured to capturemeasurements associated with the environment 204 within which the system200 is deployed and generate environmental data that serves as referencedata for implementing the dynamic weighting of various biomass estimatesfrom a plurality of biomass estimation models. In one embodiment, theenvironmental sensors of the third sensor system 202 c includes aturbidity sensor configured to measure an amount of light scattered bysuspended solids in the water.

With reference to the first set of environmental conditions in FIG. 3,as determined based at least in part on environmental data from theenvironmental sensors to identify conditions in which the weather issunny plus waters are choppy but clean (e.g., based on the turbiditysensor measurements), the processing system 210 determines that theimage data captured by the second sensor system 202 b (which ispositively influenced by, for example, ambient light due to the sunnyconditions and clear waters) will be of a relatively better quality thanthe acoustic data captured by the first sensor system 202 a (which isnegatively influenced by, for example, background sounds due to thechoppy waves which decrease the signal-to-noise ratio of acoustic data).For example, in some embodiments, the processing system 210preferentially weights the image-based, second biomass estimation modelover the acoustics-based, first biomass estimation model based on aclarity level of the water exceeding a predetermined threshold.Accordingly, the operations of block 406 include the processing system210 assigning a relative weighting with the first weighting factor w₁ of0.4 for the first biomass estimation model and the second weightingfactor w₂ of 0.6 for the second biomass estimation model to account fordifferential biomass estimates in a multi-sensor system.

As will be appreciated, environmental conditions often vary and therelative accuracy of data gathered by different sensor systems will alsovary over time. With reference to the second set of environmentalconditions in FIG. 3, as determined based at least in part onenvironmental data from the environmental sensors to identify conditionsfor a second, future period of time in which the weather is expected tobe dark (e.g., at dusk or dawn), waters have increased turbidity levels,but waves are calm, the processing system 210 determines that theimage-based, second biomass estimation model is expected to be lessaccurate in murky waters along with low ambient light levels. Thus, theprocessing system 210 will adaptively re-weight the weightings assignedto different biomass estimation models (relative to the weightingsassigned with respect to the first set of environmental conditions).Accordingly, the operations of block 406 include the processing system210 assigning a relative weighting with the first weighting factor w₁ of0.9 for the first biomass estimation model (based on acoustic data) andthe second weighting factor w₂ of 0.1 for the second biomass estimationmodel (based on image data) to discount the image-based, second biomassestimation model that is expected to be less accurate in dark and murkywaters.

It should be recognized that although the weighting of different biomassestimation models is described in the specific context of variableturbidity level measurements and biomass estimates, the operations ofblock 406 may involve weighting considerations including any number ofand any combination of parametric considerations (including data setscollected from a plurality of different environmental sensors includingphotodetectors to detect ambient light conditions and accelerometers tomeasure wave heights/swell periods, as referenced above but notexplicitly discussed) without departing from the scope of thisdisclosure. For example, such parametric environmental considerationsmay include data sets related to one or more water environmentparameters, meteorological parameters, forecasts of the same for futuretime periods, and the like.

Further, it will be appreciated that various non-environmentalconsiderations also have relevance as to the precision/accuracy ofindividual biomass estimation models, the relative precision/accuracybetween different biomass estimation models, relative availability orreliability of data captured by any of the sensor systems discussedherein, and the like. In various embodiments, sensor measurementscorresponding to different data sets (e.g., first and second parameterdata sets 108 a, 108 b) may be captured with differing temporalgranularities. For example, in one hypothetical situation and withreference back to system 200, the image-based second set of sensors maybe configured to decrease a frame rate of image capture in response tolow-bandwidth issues. However, the acoustics-based first set of sensorsmay not be subject to such performance throttling as audio filesgenerally occupy less storage space and consume less bandwidth fortransfer. In such a hypothetical situation, a rate of sensor datacapture serves as the reference parameter to be the basis for relativeweighting of different biomass attribute parameters instead of measuredenvironmental data, whereby the processing system 210 preferentiallyunderweights a biomass estimation model based on its access to a lowerquantity of data until low-bandwidth conditions are resolved. Similarly,in another hypothetical situation, the image-based second set of sensorsmay be configured to downsample captured images prior to transmission tothe processing system 210 in response to low-bandwidth issues. In such ahypothetical situation, a qualitative measure of data (such as relativeto an expected baseline) serves as the reference parameter to be thebasis for relative weighting of different biomass attribute parametersinstead of measured environmental data, whereby the processing system210 preferentially underweights a biomass estimation model based on itsaccess to a lower quality of data until low-bandwidth conditions areresolved.

Sensor measurements corresponding to different data sets (e.g., firstand second parameter data sets 108 a, 108 b) may be captured withdiffering spatial granularities. With reference back to system 200, animage-based second set of sensors may be configured such that a singlecamera is oriented to capture underwater imagery at a farming sitecontaining multiple sea cages (not shown) that each hold a differentpopulation of fish 212 in one embodiment. In another embodiment, animage-based second set of sensors may be configured such that a camerais allocated for each of the multiple sea cages. In yet anotherembodiment, an image-based second set of sensors may be configured suchthat multiple cameras are allocated for each of the multiple sea cages(e.g., each of the multiple cameras monitoring a different portionwithin the volume of the sea cage). As will be appreciated, each ofthese different embodiments captures image data at a different spatialgranularity with respect to the amount of area encompassing eachcamera's field of view. Across such embodiments, a resolution of datacapture as it relates to spatial granularity serves as the referenceparameter to be the basis for weighting of biomass attribute parameters.For example, the processing system 210 may preferentially overweight abiomass estimation model based on its access to multiple camera streamscovering multiple points of view.

It will also be appreciated that the granularity concept is applicablenot only to relative weightings between different biomass estimationmodels but also to weightings as to which reference parameters should bemore or less influential in the determination of model weightings. Forexample, with respect to spatial granularity, consider a firsthypothetical having a first set of environmental conditions such aspreviously described with reference to FIG. 3 in which the weather issunny, waters are clean, and waves are choppy. In this firsthypothetical, the environmental conditions are determined based onenvironmental sensors (e.g., using the third sensor system 202 c) thatmeasure and generate environmental data locally at a location proximateto the population of fish 212 and the processing system 210 generatesthe relative weightings of FIG. 3.

Now consider a second hypothetical having a second set of environmentalconditions similar to the first set in which the weather is sunny,waters are clean, and waves are choppy. However, in this secondhypothetical, reference data corresponding to the sunny weatherconditions is measured and generated at a weather sensing station thatis remotely located away from the location of the population of fish212. For example, in some embodiments, the weather data includes remotesensing data from satellite imagery and data, for example with aresolution of approximately 1000 square miles. In other embodiments, theweather data includes remote sensing upper air (e.g., as captured viaradiosondes and the like) data with a resolution of approximately 100square miles. In other embodiments, the weather data includes surfacecoastal station data with higher accuracy but a resolution ofapproximately 10 square miles. Further, in various embodiments, theweather data includes measurements from, for example, in-situ sensorsincluding buoys, gliders, flying drones, and the like with varyingdegrees of accuracy and resolution. As will be appreciated, the varyingspatial granularity at which these weather sensors capture data willaffect the underlying biomass estimation models and the relative weightsassigned to different biomass estimations. Accordingly, in this secondhypothetical, the processing system 210 will give less weight to thesunny weather conditions (relative to its importance with respect to thefirst hypothetical discussed above) due to the lesser spatialgranularity of sunny weather being determined by a remote weathersensor. For example, the processing system 210 may assign a secondweighting factor w₂ of less than 0.6 to the image-based, second biomassestimation model to account for an increase in uncertainty as to whetherthe sunny conditions measured by the remote sensor are indeed applicableto the local micro-climate at which the population of fish 212 arelocation.

Additionally, it will be appreciated that the various non-environmentalconsiderations discussed herein are not limited to differinggranularities at which data is collected and/or analyzed (e.g.,temporal, spatial, qualitative, quantitative, or any othercategorization in which data may be assigned relative coarse-to-fineassignations). In some embodiments, the reference data includes anactual amount of feed given for a prior time period. That is, ratherthan the reference parameter data set 208 c corresponding to measuredenvironmental data, the reference parameter data set 208 c includes datacorresponding to how much feed the population of fish 212 actually atewithin each of one or more time intervals (e.g., days, hours, minutes,or any other block of time) for which an amount of administered feed ismeasured. The one or more time intervals for which model weighting isapplied are approximately equal in length to each other, but the lengthmay vary and may be selected based on various factors includingavailability of data, desired degree of accuracy, and the like.

In various embodiments, assigning of the relative weight factors betweena first weight factor and a second weight factor (e.g., first and secondweighting factors w₁, w₂ of FIG. 3) includes assigning the first weightfactor based on a comparison of a first predicted feed amount providedby the first biomass estimation model relative to the actual amount offeed given for the prior time period. Further, assigning of the relativeweight factors includes assigning the second weight factor based on acomparison of a second predicted feed amount provided by the secondbiomass estimation model relative to the actual amount of feed given forthe prior time period. For example, in one embodiment, the actual amountof feed given for the prior time period (e.g., prior full day offeeding) corresponds to data regarding an amount of feed given to apopulation of fish by an experienced feeder. In this manner, theprocessing system compares a predicted amount of feed associated withthe first biomass estimation model from the prior day and also apredicted amount of feed associated with the second biomass estimationmodel from the prior day relative to the actual amount of feed given.Using this comparison, the processing system may assign relativeweightings to the two or more different biomass estimation models basedat least in part on their respective capabilities to predict the amountsof feed given and conversion of feed into animal biomass. That is, invarious embodiments, an amount of feed dispensed, consumed, or otherfeed-related data is utilized as a biomass indicator (such as bydetermining biomass growth based on expected or calculated feedconversion rates).

Additionally, those skilled in the art will recognize that the referenceparameter data set 208 c is not limited to data from a separate datasource (e.g., a different third sensor system 202 c that capturesenvironmental data to serve as reference data). For example, in variousembodiments, the biomass attribute data itself and/or data relationshipsbetween the first biomass attribute parameter data set 208 a and asecond biomass attribute parameter data set 208 b are derived togenerate reference data. In various embodiments, assigning of therelative weight factors between a first weight factor and a secondweight factor (e.g., first and second weighting factors w₁, w₂ of FIG.3) includes assigning the first weight factor based on a determinationof a fish's orientation in three dimensional space relative to theacoustic sensor system 202 a (e.g., based on fish pose informationdetermined from image data) at approximately the same moment of acousticdata capture.

Various parameters influence the accuracy of not only initial datacapture/measurement but also subsequent calculations based on that datasuch that small initial errors can propagate into larger downstreamprocessing errors. In the context of measuring an individual fish,relevant data capture parameters include but is not limited to one ormore of an elevation angle of a fish relative to the sensor system, aflatness level of the fish relative to the sensor system, a pose orperpendicularity of the fish relative to the sensor system, a distanceof the fish relative to the sensor system, and the like. As will beappreciated, the target strength of an acoustic target (e.g., a fish) isinfluenced by, amongst other parameters, a tilt angle and volume of itsswim bladder relative to an echo-sounder. Accordingly, in someembodiments, the adaptive weighting operations of block 406 includesidentifying fish tilt orientation in three-dimensional space relative tothe sensor system(s) (e.g., three dimensional pose to acoustic and/orthe image data sensor) and preferentially weighting biomass estimationmodels for which the pose of the target fish is conducive for increasedbiomass estimation accuracy.

In various embodiments, the processing systems described herein use oneor more learned models (not shown) to determine the relative weightingsthat should be assigned to various biomass estimation models and/or toreference parameters against which influence the various biomassestimation models. Further, additional inputs provided, or the resultsof feeding according to the estimated biomass, or both, then areincorporated into the learned model so that the learned model evolves tofacilitate the subsequent performance of similar biomass estimation. Invarious embodiments, the learned model includes a system represented byone or more data structures, executable instructions, or combinationsthereof, that is trained and having an internal representation modifiedor adapted based in input or experience during the training process. Oneexample of the learned model is a neural network. Other implementationsinclude parametric representations, such as coefficients for dynamicsmodels, latent or explicit embedding into metric spaces for methods likenearest neighbors, or the like.

In some embodiments, the learned model is initialized through asupervised learning process (e.g., a “learning by demonstration”process) so as to obtain a baseline set of knowledge regarding theoperational environment 104, 204 and the performance of at least certainbiomass estimations by the systems 100, 200. In other embodiments, thelearned model may be initiated at a particular processing system (e.g.,processing systems 110 and 210) by, for example, populating the learnedmodel with the knowledge of a learned model of other similar biomassestimation models optimized for different locales, or a “defaultknowledge core” maintained by the processing systems 110, 210 fordistribution to each biomass estimation as additional sensor systems 102and/or parameter data sets 108 are integrated into the systems 100, 200or otherwise become available to storage and processing.

With the relative weightings assigned to their respective biomassestimation models, at block 408 the processing system determines anaggregated biomass estimate based on a weighted combination of thebiomass estimates from a plurality of biomass estimation models. In thiscontext, the “weighted combination” and “aggregated biomass estimate”can be specified in various ways, depending on the goals and parametricoutputs specified by the systems 100, 200. For example, in oneembodiment and with reference to FIG. 3, determining the aggregatedbiomass estimate includes normalizing a first model biomass estimate 302a of 4.5 kgs (e.g., based on a metric unit of mass with a single decimalplace after the decimal point) and a second model biomass estimate 302 bof 11.03 lbs (e.g., based on an imperial unit of mass with two decimalplaces after the decimal point) to a common unit scale to generate anormalized biomass estimate for the first and second model biomassestimates 302 a, 302 b. In particular, the processing system 210normalizes the model biomass estimates based on a metric unit of masswith a single decimal place after the decimal point to generate a firstnormalized model biomass estimate 304 a of 4.5 kgs associated with thefirst model biomass estimate 302 a and a second normalized model biomassestimate 304 b of 5.0 kgs associated with the second model biomassestimate 302 b. Subsequently, the processing system 210 applies theassigned weighting factors w₁=0.4 and w₂=0.6 to the first normalizedmodel biomass estimate 304 a and the second normalized model biomassestimate 304 b, respectively, to generate a first weighted model biomassestimate 306 a of 1.8 kgs and a second weighted model biomass estimate306 b of 3.0 kgs. Further, the processing system 210 combines these twoweighted model biomass estimates 306 a, 306 b to generate a weighted,aggregated biomass estimate 308 of 4.8 kgs and thereby integrates datafrom multi-sensor systems to provide an aggregated biomass estimate.

Those skilled in the art will recognize that the example aggregatedbiomass estimate of FIG. 3 based on a scale having a metric unit of masswith a single decimal place after the decimal point is provided only forillustrative purposes to give a concrete example of the weighting andbiomass estimate aggregation operations discussed herein. However, anyof a variety of unit scales and/or user interface schemes may beimplemented for representing the aggregated biomass estimate. Forexample, in any of the exemplary systems disclosed here, color codingmay be used to indicate categories of any parameter. For example, in thedisplay of a user interface, color coding may be used to indicatewhether a population of fish is predicted to be underweight (e.g., withthe color red), at target weight (e.g., with the color yellow), orgaining weight better than expected (e.g., with the color green).Similarly, color coding may be used to indicate whether a feeder should,based on the aggregated biomass estimate, stop feeding (e.g., with thecolor red), begin monitoring for signs of satiation (e.g., with thecolor yellow), or begin/continue feeding (e.g., with the color green).

Returning now to numerical representations of the aggregated biomassestimate, those skilled in the art will recognize that the aggregatedbiomass estimate is not limited to providing a metric associated withbiomass levels but may instead (or additionally) prescribe specificactions to be taken. For example, in some embodiments, the processingsystem 110, 210 uses the aggregated biomass estimate to determine apredicted amount of feed to administer for a particular time period(e.g., a feeding recommendation). Such an output may be provided to, forexample, automated feeding systems to eliminate or reduce humanintervention as it associates to feeding activities. In otherembodiments, the processing system 110, 210 uses the aggregated biomassestimate to determine when a sufficient population (e.g., percentage ofpopulation) have reached a threshold size (e.g., average biomassexceeding 4 kilograms per individual fish and therefore ready to beharvested) and coordinates logistics for harvesting operations.

Further, the unit interval within a scale is not limited to being linearand in various embodiments, the unit interval is transformed to have anydesired distribution within a scale (e.g., a scale including 100 pointsfrom 0 to 100), for example, arctangent, sigmoid, sinusoidal, and thelike. In certain distributions, the intensity values increase at alinear rate along the scale, and in others, at the highest ranges theintensity values increase at more than a linear rate to indicate that itis more difficult to climb in the scale toward the extreme end of thescale. In some embodiments, the raw intensity biomass estimates arescaled by fitting a curve to a selected group of canonical exerciseroutines that are predefined to have particular intensity biomassestimates.

Thus, the operations of method 400 provides variable and relativeweighting to different biomass estimates from different biomassestimation models and combines them into an aggregated biomass estimatethat is more accurate than would be individually provided by eachbiomass estimation model by itself. It should be noted that the method400 is illustrated as a single instance of relative weighting betweentwo biomass estimation models based on a single reference parameter forease of illustration and description. However, in some embodiments,after a single pass through the operations of blocks 402-408 iscompleted for determining a biomass estimate for a first time period,the operations of blocks 402-408 may be repeated for additional passesto determine a biomass estimate for a second time period (e.g., such asto provide continually updating biomass estimates) or a different timeperiod interval (e.g., such as to provide more or less granular biomassestimates). In some embodiments, after a single pass through theoperations of blocks 402-408 is completed for determining a biomassestimate, the operations of blocks 402-408 may be repeated forre-weighting as environmental conditions change, refining of relativeweightings to adjust for additional factors (e.g., consideration of morethan one reference parameter data set), and the like.

Additionally, in various embodiments, at block 410 the processing systemoptionally generates (as represented by the dotted line box) a biomasstriggered instruction signal based at least in part on the aggregatedbiomass estimate that instructs a user and/or one or more farm systemsregarding specific actions to be taken in accordance to the biomassestimate (e.g., as quantified by the aggregated biomass estimate). Forexample, with respect to FIG. 3 as previously discussed, the biomasstriggered instruction signal 310 (as represented by the dotted line box)based at least in part on the aggregated biomass estimate 308 instructsan automated feeding system regarding specific actions to be taken inaccordance to the biomass estimate (e.g., change an amount of feeddispensed, change a formulation of feed dispensed, and the like).

It should be recognized that although biomass estimation and aggregatedbiomass estimation has been primarily discussed here in the context ofsensors capturing different data types and within similar locations(e.g., underwater sensors of FIG. 2), any combination of sensorsincluding multiple sensors capturing similar data and multi-locationsensors may be employed for any of a variety of possible configurationswithout departing from the scope of this disclosure. For example, andnow referring to FIG. 5, illustrated is a diagram showing a system 500implementing two sets of image-based sensors in accordance with someembodiments. In various embodiments, the system 500 includes a pluralityof sensor systems 502 that are each configured to monitor and generatedata associated with the environment 504 within which they are placed.

As shown, the plurality of sensor systems 502 includes a first sensorsystem 502 a positioned below the water surface 506 and including afirst set of one or more sensors. The first set of one or more sensorsare configured to monitor the environment 504 below the water surface506 and to observe fish behavior and capture measurements associatedwith biomass attribute parameters related to fish biomass. It will beappreciated that biomass attribute parameters, in various embodiments,include one or more parameters corresponding to the environment 504within which the one or more sensors are positioned and may be measured(or otherwise captured and detected) to generate parameter data sets 508to be used in biomass estimation models.

In various embodiments, the first sensor system 502 a of FIG. 5 includesone or more imaging sensors configured to observe fish behavior andcapture measurements associated with biomass attribute parametersrelated to fish biomass. In various embodiments, the imaging sensors areconfigured to capture image data corresponding to, for example, thepresence (or absence), abundance, distribution, size, and behavior ofunderwater objects (e.g., a population of fish 512 as illustrated inFIG. 5). In various embodiments, such captured image data is processedto extract various measurements corresponding to biomass attributes(e.g., various image-related biomass attributes such as previouslydescribed with respect to FIG. 1). It should be recognized that althoughspecific sensors are described below for illustrative purposes, variousimaging and non-imaging sensors may be implemented in the systemsdescribed herein without departing from the scope of this disclosure.

In some embodiments, the imaging sensors of the first sensor system 502a includes one or more cameras configured to capture still images and/orrecord moving images (e.g., video data). The one or more cameras aredirected towards the surrounding environment 504 below the water surface506, with each camera capturing a sequence of images (e.g., videoframes) of the environment 504 and any objects in the environment. Invarious embodiments, each camera has a different viewpoint or pose(i.e., location and orientation) with respect to the environment.Although FIG. 5 only shows a single camera for ease of illustration anddescription, persons of ordinary skill in the art having benefit of thepresent disclosure should appreciate that the first sensor system 502 acan include any number of cameras and which may account for parameterssuch as each camera's horizontal field of view, vertical field of view,and the like. Further, persons of ordinary skill in the art havingbenefit of the present disclosure should appreciate that the firstsensor system 502 b can include any arrangement of cameras (e.g.,cameras positioned on different planes relative to each other,single-plane arrangements, spherical configurations, and the like).

In various embodiments, the imaging sensors of the first sensor system502 a includes a first camera (or lens) having a particular field ofview 518 as represented by the dashed lines that define the outer edgesof the camera's field of view that images the environment 504 or atleast a portion thereof. For the sake of clarity, only the field of view518 for a single camera is illustrated in FIG. 5. In variousembodiments, the imaging sensors of the first sensor system 502 aincludes at least a second camera having a different but overlappingfield of view (not shown) relative to the first camera (or lens). Imagesfrom the two cameras therefore form a stereoscopic pair for providing astereoscopic view of objects in the overlapping field of view. Further,it should be recognized that the overlapping field of view is notrestricted to being shared between only two cameras. For example, atleast a portion of the field of view 518 of the first camera of thefirst sensor system 502 a may, in some embodiments, overlap with thefields of view of two other cameras to form an overlapping field of viewwith three different perspectives of the environment 504.

In some embodiments, the imaging sensors of the first sensor system 502a includes one or more light field cameras configured to capture lightfield data emanating from the surrounding environment 504. In otherwords, the one or more light field cameras captures data not only withrespect to the intensity of light in a scene (e.g., the light fieldcamera's field of view/perspective of the environment) but also thedirections of light rays traveling in space. In contrast, conventionalcameras generally record only light intensity data. In otherembodiments, the imaging sensors of the first sensor system 502 aincludes one or more range imaging cameras (e.g., time-of-flight andLIDAR cameras) configured to determine distances between the camera andthe subject for each pixel of captured images. For example, such rangeimaging cameras may include an illumination unit (e.g., some artificiallight source) to illuminate the scene and an image sensor with eachpixel measuring the amount of time light has taken to travel from theillumination unit to objects in the scene and then back to the imagesensor of the range imaging camera.

It should be noted that the various operations are described here in thecontext of multi-camera or multi-lens cameras for ease of descriptionand illustration. However, it should be recognized that the operationsdescribed herein may similarly be implemented with any type of imagingsensor without departing from the scope of this disclosure. For example,in various embodiments, the imaging sensors of the first sensor system502 a may include, but are not limited to, any of a number of types ofoptical cameras (e.g., RGB and infrared), thermal cameras, range- anddistance-finding cameras (e.g., based on acoustics, laser, radar, andthe like), stereo cameras, structured light cameras, ToF cameras,CCD-based cameras, CMOS-based cameras, machine vision systems, lightcurtains, multi- and hyper-spectral cameras, thermal cameras, and thelike.

Additionally, as illustrated in FIG. 5, the plurality of sensor systems502 includes a second sensor system 502 b positioned above the watersurface 506 and including a second set of one or more sensors. Thesecond set of one or more sensors are configured to monitor theenvironment 504 proximate to (e.g., at the water surface or evenslightly underwater if the one or more sensors are capable of imaging)and above the water surface 506 and generate data associated with asecond biomass attribute parameter. In particular, the second sensorsystem 502 b of FIG. 5 includes one or more imaging sensors configuredto observe fish behavior and capture measurements associated withbiomass attribute parameters related to fish biomass. In variousembodiments, the imaging sensors are configured to capture image datacorresponding to, for example, the presence (or absence), abundance,distribution, size, and behavior of objects (e.g., a population of fish512 as illustrated in FIG. 5). In various embodiments, such capturedimage data is processed to extract various measurements corresponding tobiomass attributes (e.g., various image-related biomass attributes suchas previously described with respect to FIG. 1). It should be recognizedthat although specific sensors are described below for illustrativepurposes, various imaging and non-imaging sensors may be implemented inthe systems described herein without departing from the scope of thisdisclosure.

In some embodiments, the imaging sensors of the second sensor system 502b includes one or more cameras configured to capture still images and/orrecord moving images (e.g., video data). The one or more cameras aredirected towards the environment proximate the water surface 506, witheach camera capturing a sequence of images (e.g., video frames) of theenvironment 504 and any objects in the environment. In variousembodiments, each camera has a different viewpoint or pose (i.e.,location and orientation) with respect to the environment. Although FIG.5 only shows a single camera for ease of illustration and description,persons of ordinary skill in the art having benefit of the presentdisclosure should appreciate that the second sensor system 502 b caninclude any number of cameras and which may account for parameters suchas each camera's horizontal field of view, vertical field of view, andthe like. Further, persons of ordinary skill in the art having benefitof the present disclosure should appreciate that the second sensorsystem 502 b can include any arrangement of cameras (e.g., cameraspositioned on different planes relative to each other, single-planearrangements, spherical configurations, and the like).

In various embodiments, the imaging sensors of the second sensor system502 b includes a first camera (or lens) having a particular field ofview 520 as represented by the dashed lines that define the outer edgesof the camera's field of view that images the environment 504 or atleast a portion thereof. For the sake of clarity, only the field of view520 for a single camera is illustrated in FIG. 5. In variousembodiments, the imaging sensors of the second sensor system 502 bincludes at least a second camera (or lens) having a different butoverlapping field of view (not shown) relative to the first camera (orlens). Images from the two cameras therefore form a stereoscopic pairfor providing a stereoscopic view of objects in the overlapping field ofview. Further, it should be recognized that the overlapping field ofview is not restricted to being shared between only two cameras. Forexample, at least a portion of the field of view 520 of the first cameraof the second sensor system 502 b may, in some embodiments, overlap withthe fields of view of two other cameras to form an overlapping field ofview with three different perspectives of the environment 504.

In some embodiments, the imaging sensors of the second sensor system 502b includes one or more light field cameras configured to capture lightfield data emanating from the surrounding environment 504. In otherwords, the one or more light field cameras captures data not only withrespect to the intensity of light in a scene (e.g., the light fieldcamera's field of view/perspective of the environment) but also thedirections of light rays traveling in space. In contrast, conventionalcameras generally record only light intensity data. In otherembodiments, the imaging sensors of the second sensor system 502 bincludes one or more range imaging cameras (e.g., time-of-flight andLIDAR cameras) configured to determine distances between the camera andthe subject for each pixel of captured images. For example, such rangeimaging cameras may include an illumination unit (e.g., some artificiallight source) to illuminate the scene and an image sensor with eachpixel measuring the amount of time light has taken to travel from theillumination unit to objects in the scene and then back to the imagesensor of the range imaging camera.

It should be noted that the various operations are described here in thecontext of multi-camera or multi-lens cameras for ease of descriptionand illustration. However, it should be recognized that the operationsdescribed herein may similarly be implemented with any type of imagingsensor without departing from the scope of this disclosure. For example,in various embodiments, the imaging sensors of the second sensor system502 b may include, but are not limited to, any of a number of types ofoptical cameras (e.g., RGB and infrared), thermal cameras, range- anddistance-finding cameras (e.g., based on acoustics, laser, radar, andthe like), stereo cameras, structured light cameras, ToF cameras,CCD-based cameras, CMOS-based cameras, machine vision systems, lightcurtains, multi- and hyper-spectral cameras, thermal cameras, and thelike.

Additionally, as illustrated in FIG. 5, the plurality of sensor systems502 includes a third sensor system 502 c including a third set of one ormore sensors. As described in further detail herein, in variousembodiments, the environmental sensors of the third sensor system 502 cgenerate environmental data that serves as reference data forimplementing the dynamic weighting of various biomass estimates from aplurality of biomass estimation models. For example, in one embodiment,the environmental sensors of the third sensor system 502 c includes anambient light sensor or other photodetector configured to sense orotherwise measure an amount of ambient light present within theenvironment local to the sensor. It should be recognized that althoughFIG. 5 is described in the specific context of an ambient light sensor,the third sensor system 502 c may include any number of and anycombination of various environmental sensors without departing from thescope of this disclosure.

Further, the plurality of sensor systems 502 includes a fourth sensorsystem 502 d including a fourth set of one or more sensors. The fourthset of one or more sensors are configured to monitor the environment 504below the water surface 506 and generate data associated with areference parameter. As described in further detail herein, in variousembodiments, the environmental sensors of the fourth sensor system 502 dgenerate environmental data that serves as reference data forimplementing the dynamic weighting of various biomass estimates from aplurality of biomass estimation models. For example, in one embodiment,the environmental sensors of the fourth sensor system 502 d includes aturbidity sensor configured to measure an amount of light scattered bysuspended solids in the water. In general, the more total suspendedparticulates or solids in water, the higher the turbidity and thereforemurkier the water appears. It should be recognized that although FIG. 5is described in the specific context of a turbidity sensor, the fourthsensor system 502 d may include any number of and any combination ofvarious environmental sensors without departing from the scope of thisdisclosure.

The first sensor system 502 a and the second sensor system 502 b eachgenerate a first biomass attribute parameter data set 508 a and a secondbiomass attribute parameter data set 508 b, respectively. In the contextof FIG. 5, the first biomass attribute parameter includes image datacaptured from below the water surface 506 and the second biomassattribute parameter includes image data captured with respect to thewater surface 506 or from above the water surface 506. Such image datamay include any image-related value or other measurablefactor/characteristic that is representative of at least a portion of adata set that describes the presence (or absence), abundance,distribution, size, and/or behavior of objects (e.g., a population offish 512 as illustrated in FIG. 5).

For example, in various embodiments, the image data of the first andsecond biomass attribute parameter data sets 508 a, 508 b includescamera images capturing measurements representative of the relativeand/or absolute locations of individual fish of the population of fish512 within the environment 504. The image data may also include cameraimages capturing measurements representative of the behavior ofindividual fish of the population of fish 512. For example, the imagedata may include camera images capturing data related to one or morebiomass attributes of individual fish of the population of fish 512within the environment 504. It should be recognized that although thefirst biomass attribute parameter and the second biomass attributeparameter has been abstracted and described here generally as “imagedata” for ease of description, those skilled in the art will understandthat image data (and therefore the first biomass attribute parameterdata set 508 a and the second biomass attribute parameter data set 508 bcorresponding to the image data) may include, but is not limited to, anyof a plurality of image frames, extrinsic parameters defining thelocation and orientation of the image sensors, intrinsic parameters thatallow a mapping between camera coordinates and pixel coordinates in animage frame, camera models, operational parameters of the image sensors(e.g., shutter speed), depth maps, and the like.

Similarly, in the context of FIG. 5, the reference parameter includesenvironmental data. Such environmental data may include any measurementrepresentative of the environment 504 within which the environmentalsensors are deployed. For example, the environmental data (and thereforethe first reference parameter data set 508 c and the second referenceparameter data set 508 d corresponding to the environmental data) mayinclude, but is not limited to, any of a plurality of ambient lightmeasurements, water turbidity measurements, water temperaturemeasurements, metocean measurements, satellite weather measurements,weather forecasts, air temperature, dissolved oxygen, current direction,current speeds, and the like.

In various embodiments, the processing system 510 receives one or moreof the data sets 508 (e.g., first biomass attribute parameter data set508 a, the second biomass attribute parameter data set 508 b, the firstreference parameter data set 508 c, and the second reference parameterdata set 508 d) via, for example, wired-telemetry, wireless-telemetry,or any other communications links for processing. The processing system510 provides the first biomass attribute parameter data set 508 a to afirst biomass estimation model 522 a. The processing system 510 alsoprovides the second biomass attribute parameter data set 508 b to asecond biomass estimation model 522 b different from the first biomassestimation model 522 a. In various embodiments, the first biomassestimation model 522 a receives the image data of the first biomassattribute parameter data set 508 a as input and generates a firstbiomass estimate 524 a. By way of non-limiting example, in someembodiments, the first biomass estimation model 522 a utilizes imagedata related to containing measurements related to one or more biomassattributes of individual fish (or with respect to two or more fish) forgenerating the first biomass estimate 524 a. In various embodiments, thefirst biomass estimate 524 a is a numerical description of a body weightof an individual fish. In some embodiments, the first biomass estimate524 a is a numerical description of an average body weight for fishwithin the population of fish 512.

The processing system 510 also provides the second biomass attributeparameter data set 508 b to a second biomass estimation model 522 b. Invarious embodiments, the second biomass estimation model 522 b receivesthe image data of the second biomass attribute parameter data set 508 bas input and generates a second biomass estimation 524 b. By way ofnon-limiting example, in some embodiments, the second biomass estimationmodel 522 b utilizes image data containing measurements related to oneor more biomass attributes of individual fish (or with respect to two ormore fish) for generating the second biomass estimate 524 b. In variousembodiments, the second biomass estimate 524 b is a numericaldescription of a body weight of an individual fish. In some embodiments,the second biomass estimate 524 b is a numerical description of anaverage body weight for fish within the population of fish 512. In someexamples, for example, the image data captured by the second set ofsensors 502 b may be analyzed to quantify or otherwise determine a levelof surface level activity exhibited by the fish 512 (e.g., resultingfrom fish jumping out of the water as illustrated, rolling along thewater surface 506, splashes at the water surface 506 c caused byjumping, and the like) as a biomass attribute for generating the secondbiomass estimate 224 b (e.g., an amount of total activity beingcorrelated with total biomass within a marine enclosure housing the fish512).

Subsequently, such as previously discussed in more detail with referenceto FIGS. 3 and 4, the processing system 510 adaptively weights the firstbiomass estimate 524 a of the first biomass estimation model 522 a witha first weighting factor relative to a second weighting factor for asecond biomass estimate 524 b of the second biomass estimation model 522b in order to determine an aggregated biomass estimate 526 based on acombination of the first biomass estimation model 522 a using the firstweight factor and the second biomass estimation model 522 b using thesecond weight factor. For example, in one embodiment, the processingsystem 510 may preferentially weight the second biomass estimation model522 b (i.e., model based on surface camera images) relative to the firstbiomass estimation model 522 a (i.e., model based on sub-surface cameraimages) when environmental conditions are indicated by the referencesensors 502 c, 502 d to include turbid waters but sunny weather at noon.Similarly, the processing system 510 may preferentially underweight thesecond biomass estimation model 522 b (i.e., model based on surfacecamera images) relative to the first biomass estimation model 522 a(i.e., model based on sub-surface camera images) when environmentalconditions are indicated by the reference sensors 502 c, 502 d toinclude clear waters but foggy weather conditions such that the surfacecameras of the second sensor system 502 b will be less reliable.

It should be recognized that although biomass estimation and aggregatedbiomass estimation has been primarily discussed in the context ofsensors capturing data for aquaculture biomass estimation, the conceptsdescribed herein may similarly be employed for any of a variety offarming environments without departing from the scope of thisdisclosure. For example, and now referring to FIG. 6, illustrated is adiagram showing a terrestrial biomass estimation system 600 implementingdifferent sensors for livestock biomass estimation in accordance withsome embodiments.

In various embodiments, the system 600 includes a plurality of sensorsystems 602 that are each configured to monitor and generate dataassociated with the environment 604 within which they are placed. Asshown, the plurality of sensor systems 602 includes a first sensorsystem 602 a including a first set of one or more sensors. The first setof one or more sensors are configured to monitor the environment 604within which they are placed and generate data associated with a firstbiomass attribute parameter. Additionally, the plurality of sensorsystems 602 includes a second sensor system 602 b including a second setof one or more sensors. The second set of one or more sensors areconfigured to monitor the environment 604 within which they are placedand generate data associated with a second biomass attribute parameter.

In particular, the first sensor system 602 a of FIG. 6 includes one ormore imaging sensors configured to observe animal behavior and capturemeasurements associated with biomass attribute parameters related toanimal biomass. As used herein, the term “animal” generally includes anylivestock that is raised including fish, poultry, pigs, horses, birds,insects, and the like. Animals include aquaculture animals such as fish(such as previously described with respect to FIGS. 1-5) and shrimp,beef cattle, poultry, swine, and any other farm animal raised for profitand/or meat production. Further, animals also include livestock raisedor nurtured for producing a product for consumption, such as dairycattle (e.g., production of milk), poultry (e.g., eggs), and fish (e.g.,roe for consumption). In particular, the one or more sensor systems 602are positioned for monitoring properties associated with animals 612within or proximate to a farming enclosure (e.g., livestock barn 606).

In various embodiments, the imaging sensors are configured to captureimage data corresponding to, for example, the presence (or absence),abundance, distribution, size, and behavior of terrestrial objects(e.g., a population of animals 612 as illustrated in FIG. 6). In variousembodiments, such captured image data is processed to extract variousmeasurements corresponding to biomass attributes (e.g., variousimage-related biomass attributes such as previously described withrespect to FIG. 1). It should be recognized that although specificsensors are described below for illustrative purposes, various imagingsensors may be implemented in the systems described herein withoutdeparting from the scope of this disclosure.

In some embodiments, the imaging sensors of the first sensor system 602a includes one or more cameras configured to capture still images and/orrecord moving images (e.g., video data). The one or more cameras aredirected towards the surrounding environment 604, with each cameracapturing a sequence of images (e.g., video frames) of the environment604 and any objects in the environment. In various embodiments, eachcamera has a different viewpoint or pose (i.e., location andorientation) with respect to the environment. Although FIG. 6 only showsa single camera for ease of illustration and description, persons ofordinary skill in the art having benefit of the present disclosureshould appreciate that the first sensor system 602 a can include anynumber of cameras and which may account for parameters such as eachcamera's horizontal field of view, vertical field of view, and the like.Further, persons of ordinary skill in the art having benefit of thepresent disclosure should appreciate that the first sensor system 602 acan include any arrangement of cameras (e.g., cameras positioned ondifferent planes relative to each other, single-plane arrangements,spherical configurations, and the like).

In various embodiments, the imaging sensors of the first sensor system602 a includes a first camera (or lens) having a particular field ofview 618 as represented by the dashed lines that define the outer edgesof the camera's field of view that images the environment 604 or atleast a portion thereof. For the sake of clarity, only the field of view618 for a single camera is illustrated in FIG. 6. In variousembodiments, the imaging sensors of the first sensor system 602 aincludes at least a second camera having a different but overlappingfield of view (not shown) relative to the first camera (or lens). Imagesfrom the two cameras therefore form a stereoscopic pair for providing astereoscopic view of objects in the overlapping field of view. Further,it should be recognized that the overlapping field of view is notrestricted to being shared between only two cameras. For example, atleast a portion of the field of view 618 of the first camera of thefirst sensor system 602 a may, in some embodiments, overlap with thefields of view of two other cameras to form an overlapping field of viewwith three different perspectives of the environment 604.

In some embodiments, the imaging sensors of the first sensor system 602a includes one or more light field cameras configured to capture lightfield data emanating from the surrounding environment 604. In otherwords, the one or more light field cameras captures data not only withrespect to the intensity of light in a scene (e.g., the light fieldcamera's field of view/perspective of the environment) but also thedirections of light rays traveling in space. In contrast, conventionalcameras generally record only light intensity data. In otherembodiments, the imaging sensors of the first sensor system 602 aincludes one or more range imaging cameras (e.g., time-of-flight andLIDAR cameras) configured to determine distances between the camera andthe subject for each pixel of captured images. For example, such rangeimaging cameras may include an illumination unit (e.g., some artificiallight source) to illuminate the scene and an image sensor with eachpixel measuring the amount of time light has taken to travel from theillumination unit to objects in the scene and then back to the imagesensor of the range imaging camera.

It should be noted that the various operations are described here in thecontext of multi-camera or multi-lens cameras. However, it should berecognized that the operations described herein may similarly beimplemented with any type of imaging sensor without departing from thescope of this disclosure. For example, in various embodiments, theimaging sensors of the first sensor system 602 a may include, but arenot limited to, any of a number of types of optical cameras (e.g., RGBand infrared), thermal cameras, range- and distance-finding cameras(e.g., based on acoustics, laser, radar, and the like), stereo cameras,structured light cameras, ToF cameras, CCD-based cameras, CMOS-basedcameras, machine vision systems, light curtains, multi- andhyper-spectral cameras, thermal cameras, other machine vision systemsthat are operable along various portions of the electromagnetic spectrum(including visible light, infrared, and other bands), sonar cameras(e.g., DIDSON and ARTS sonar cameras), and the like.

In various embodiments, the second sensor system 602 b includes one ormore three dimensional (3D) surface sensors configured to observe animalbehavior and capture measurements associated with biomass attributeparameters related to animal biomass. In various embodiments, suchcaptured 3D surface data is processed to extract various measurementscorresponding to biomass attributes (e.g., various image-related biomassattributes such as previously described with respect to FIG. 1). Itshould be recognized that although specific sensors are described belowfor illustrative purposes, various 3D surface sensors may be implementedin the systems described herein without departing from the scope of thisdisclosure.

In various embodiments, the 3D surface sensors of the second sensorsystem 602 b includes one or more of contact and non-contact based 3Dsurface measurement systems. Contact based 3D surface sensors include,for example, mechanical arms with touch sensitive tips for touchscanning of object surfaces to generate 3D data point. Similarly, invarious embodiments, contact sensors (not necessarily for surfacesensing) include collars, tags, bioelectrical impedance sensors tomeasure fat versus lean body mass (e.g., via body conductance/impedancemeasurements), and the like for measuring various biomass attributes.

Non-contact, non-invasive 3D surface sensors include but are not limitedto reflective optical sensors (e.g., structured light sensors),reflective non-optical sensors (e.g., sonar, imaging radar, and otherultrasonic sensors), non-contact emitted imaging sensors (e.g., infraredand thermal sensors), passive reflective sensors that rely on ambientlight and surface texture of targets to capture dimensional data (e.g.,passive stereo, passive depth from focus/defocus, shape from shading,and the like), active reflective sensors utilizing a controlled lightsource (e.g., pulsed or modulated light, interferometry sensors, activedepth-from-focus/defocus sensors, active triangulation sensors such as3D laser scanners, active stereoscopic vision systems, and LIDAR), andthe like.

In various embodiments, the 3D surface sensor of the second sensorsystem 602 b includes a first laser scanner having a particular field ofview 620 as represented by the dashed lines that define the outer edgesof the laser scanner's field of view that images the environment 604 orat least a portion thereof. For the sake of clarity, only the field ofview 620 for a single 3D surface sensor is illustrated in FIG. 6. Invarious embodiments, the 3D surface sensors of the second sensor system602 b includes at least a second laser scanner having a different fieldof view relative to the first laser scanner. Further, it should berecognized that the fields of view are not restricted to being sharedbetween only two laser scanners. For example, at least a portion of thefield of view 620 of the first camera of the second sensor system 602 bmay, in some embodiments, overlap with the fields of view of two otherlaser scanners to form an overlapping field of view with three differentperspectives of the environment 604.

Additionally, as illustrated in FIG. 6, the plurality of sensor systems602 includes a third sensor system 602 c including a third set of one ormore sensors. The third set of one or more sensors are configured tomonitor the environment 604 proximate the sensor systems 602 andgenerate data associated with a reference parameter. In particular, thethird sensor system 602 c of FIG. 6 includes one or more environmentalsensors configured to capture measurements associated with theenvironment 604 within which the system 600 is deployed. As described infurther detail below, in various embodiments, the environmental sensorsof the third sensor system 602 c generate environmental data that servesas reference data for implementing the dynamic weighting of variousestimates from a plurality of biomass estimation models.

For example, in one embodiment, the environmental sensors of the thirdsensor system 602 c includes an ambient light sensor or otherphotodetector configured to sense or otherwise measure an amount ofambient light present within the environment local to the sensor. Itshould be recognized that although FIG. 6 is described in the specificcontext of an ambient light sensor, the third sensor system 602 c mayinclude any number of and any combination of various environmentalsensors without departing from the scope of this disclosure.

The first sensor system 602 a and the second sensor system 602 b eachgenerate a first biomass attribute parameter data set 608 a and a secondbiomass attribute parameter data set 608 b, respectively, representingvolumetric, curvilinear (e.g., surface), linear measurements oflivestock animals, and the like. In the context of FIG. 6, the firstbiomass attribute parameter includes image data. The image data mayinclude any image-related value or other measurable factor orcharacteristic that is representative of at least a portion of a dataset that describes the presence (or absence), abundance, distribution,size, and/or behavior of objects (e.g., a population of animals 612 asillustrated in FIG. 5). For example, the image data may include cameraimages capturing data related to one or more biomass attributes ofindividual animals of the population of animals 612 within theenvironment 604. It should be recognized that although the first biomassattribute parameter has been abstracted and described here generally as“image data” for ease of description, those skilled in the art willunderstand that image data (and therefore the first biomass attributeparameter data set 608 a corresponding to the image data) may include,but is not limited to, any of a plurality of image frames, extrinsicparameters defining the location and orientation of the image sensors,intrinsic parameters that allow a mapping between camera coordinates andpixel coordinates in an image frame, camera models, operationalparameters of the image sensors (e.g., shutter speed), depth maps, andthe like.

In the context of FIG. 6, the second biomass attribute parameterincludes 3D surface data. In some embodiments, the laser scanner(s) ofthe second sensor system 602 b generate 3D point cloud data. Forexample, the 3D surface data may include laser scans capturing datarelated to one or more biomass attributes of individual animals of thepopulation of fish 612 within the environment 604. 3D point clouds areusually used to detect, other attributes, the shape and pose of targetobject in 3D coordinate systems. Although described here in the contextof laser scanners, 3D point clouds may be generated using variousvolumetric sensors including but not limited to stereo cameras,time-of-flight (ToF) cameras, and any other sensor capable of capturingshape data of objects to create point clouds from the surface of theobject. In various embodiments, pout cloud data includes coordinateInformation, point color information, point normal orientation, and thelike. As will be appreciated, point cloud data includes volumetric datawhich is relevant for animal shape or conformation determination inestimating animal biomass.

It should be recognized that although the second biomass attributeparameter has been abstracted and described here generally as “3Dsurface data” for ease of description, those skilled in the art willunderstand that 3D surface data (and therefore the second biomassattribute parameter data set 608 b corresponding to the 3D surface data)may include, but is not limited to, any of a plurality of laser scans,extrinsic parameters defining the location and orientation of the laserscanners, intrinsic parameters that allow a mapping between laserscanner coordinates and point cloud coordinates, laser scanner models,operational parameters of the laser scanners, depth maps, and the like.

Similarly, in the context of FIG. 6, the reference parameter includesenvironmental data. Such environmental data may include any measurementrepresentative of the environment 604 within which the environmentalsensors are deployed. For example, the environmental data (and thereforethe reference parameter data set 608 c corresponding to theenvironmental data) may include, but is not limited to, any of aplurality of ambient light measurements, water turbidity measurements,water temperature measurements, metocean measurements, satellite weathermeasurements, weather forecasts, air temperature, dissolved oxygen,current direction, current speeds, and the like.

In various embodiments, the processing system 610 receives one or moreof the data sets 608 (e.g., first biomass attribute parameter data set608 a, the second biomass attribute parameter data set 608 b, and thereference parameter data set 608 c) via, for example, wired-telemetry,wireless-telemetry, or any other communications links for processing.The processing system 610 provides the first biomass attribute parameterdata set 608 a to a first biomass estimation model 622 a. The processingsystem 610 also provides the second biomass attribute parameter data set608 b to a second biomass estimation model 622 b different from thefirst biomass estimation model 622 a. In various embodiments, the firstbiomass estimation model 622 a receives the image data of the firstbiomass attribute parameter data set 608 a as input and generates afirst biomass estimate 624 a. By way of non-limiting example, in someembodiments, the first biomass estimation model 622 a utilizes imagedata related to containing measurements related to one or more biomassattributes of individual animals (or with respect to two or more fish)for generating the first biomass estimate 624 a. In various embodiments,the first biomass estimate 624 a is a numerical description of a bodyweight of an individual animal. In some embodiments, the first biomassestimate 624 a is a numerical description of an average body weight foranimals within the population of animals 612.

The processing system 610 also provides the second biomass attributeparameter data set 608 b to a second biomass estimation model 622 b. Invarious embodiments, the second biomass estimation model 622 b receivesthe 3D surface data of the second biomass attribute parameter data set608 b as input and generates a second biomass estimation 624 b. By wayof non-limiting example, in some embodiments, the second biomassestimation model 622 b utilizes 3D surface data containing measurementsrelated to one or more biomass attributes of individual animals (or withrespect to two or more fish) for generating the second biomass estimate624 b. In various embodiments, the second biomass estimate 624 b is anumerical description of a body weight of an individual animal. In someembodiments, the second biomass estimate 624 b is a numericaldescription of an average body weight for animals within the populationof animals 612. In some examples, for example, the 3D surface datacaptured by the second set of sensors 602 b may be analyzed to quantifyor otherwise generate 3D point cloud data and volumetric animal data asa biomass attribute for generating the second biomass estimate 624 b.

Subsequently, such as previously discussed in more detail with referenceto FIGS. 3 and 4, the processing system 610 adaptively weights the firstbiomass estimate 624 a of the first biomass estimation model 622 a witha first weighting factor relative to a second weighting factor for asecond biomass estimate 624 b of the second biomass estimation model 622b in order to determine an aggregated biomass estimate 626 based on acombination of the first biomass estimation model 622 a using the firstweight factor and the second biomass estimation model 622 b using thesecond weight factor. For example, in one embodiment, the processingsystem 610 may preferentially weight the second biomass estimation model622 b (i.e., model based on 3D surface data) relative to the firstbiomass estimation model 622 a (i.e., model based on image data) whenenvironmental conditions are indicated by the reference sensor 602 c toinclude dim ambient light conditions that disproportionately reducequality of optical image capture of the first sensor system 602 arelative to laser triangulation by the second sensor system 602 b.Similarly, the processing system 610 may preferentially underweight thesecond biomass estimation model 622 b (i.e., model based on 3D surfaceimages) relative to the first biomass estimation model 622 a (i.e.,model based on optical camera images) when environmental conditions areindicated by the reference sensor 602 c to include extremely sunnyconditions that provide excellent lighting conditions for the firstsensor system 602 a but may introduce a significant amount of energysuch that some of the points in the 3D point cloud being to saturate,thereby rendering the second sensor system 602 b to be less reliable (orat least have less of an accuracy improvement over optical systemsrelative to dim ambient light conditions).

FIG. 7 is a block diagram illustrating a system 700 configured toprovide a biomass estimation in accordance with some embodiments. Insome embodiments, the system 700 includes one or more computingplatforms 702. The computing platform(s) 702 may be configured tocommunicate with one or more remote platforms 704 according to aclient/server architecture, a peer-to-peer architecture, and/or otherarchitectures via a network 724. Remote platform(s) 704 may beconfigured to communicate with other remote platforms via computingplatform(s) 702 and/or according to a client/server architecture, apeer-to-peer architecture, and/or other architectures via the network724. Users may access system 700 via remote platform(s) 704. A givenremote platform 704 may include one or more processors configured toexecute computer program modules. The computer program modules may beconfigured to enable an expert or user associated with the given remoteplatform 704 to interface with system 700 and/or one or more externalresource(s) 706, and/or provide other functionality attributed herein toremote platform(s) 704. By way of non-limiting example, a given remoteplatform 704 and/or a given computing platform 702 may include one ormore of a server, a desktop computer, a laptop computer, a handheldcomputer, a tablet computing platform, a NetBook, a Smartphone, a gamingconsole, and/or other computing platforms.

In some implementations, the computing platform(s) 702, remoteplatform(s) 704, and/or one or more external resource(s) 706 may beoperatively linked via one or more electronic communication links. Forexample, such electronic communication links may be established, atleast in part, via a network 724 such as the Internet and/or othernetworks. It will be appreciated that this is not intended to belimiting, and that the scope of this disclosure includes implementationsin which computing platform(s) 702, remote platform(s) 704, and/or oneor more external resource(s) 706 may be operatively linked via someother communication media. External resource(s) 706 may include sourcesof information outside of system 700, external entities participatingwith system 700, and/or other resources. In some implementations, someor all of the functionality attributed herein to external resources 706may be provided by resources included in system 700.

In various embodiments, the computing platform(s) 702 are configured bymachine-readable instructions 708 including one or more instructionmodules. In some embodiments, the instruction modules include computerprogram modules for implementing the various operations discussed herein(such as the operations previously discussed with respect to FIG. 4).For purposes of reference, the instruction modules include one or moreof a first biomass attribute parameter module 710, a second biomassattribute parameter module 712, a first biomass estimation module 714, asecond biomass estimation module 716, a reference parameter module 718,a dynamic weighting module 720, and an aggregated biomass estimatemodule 722. Each of these modules may be implemented as one or moreseparate software programs, or one or more of these modules may beimplemented in the same software program or set of software programs.Moreover, while referenced as separate modules based on their overallfunctionality, it will be appreciated that the functionality ascribed toany given model may be distributed over more than one software program.For example, one software program may handle a subset of thefunctionality of the first biomass attribute parameter module 710 whileanother software program handles another subset of the functionality ofthe first biomass attribute parameter module 710 and the functionalityof the first biomass estimation module 714.

In various embodiments, the first biomass attribute parameter module 710generally represents executable instructions configured to receive afirst biomass attribute parameter data set associated with a firstbiomass attribute parameter. With reference to FIGS. 1-6 and 8-9, invarious embodiments, the first biomass attribute parameter module 710receives sensor data including the first biomass attribute parameterdata set via a wireless or wired communications link for storage,further processing, and/or distribution to other modules of the system700. For example, in the context of FIG. 2, the sensor system 202communicates at least the first parameter data set 208 a includingacoustic data corresponding to the presence (or absence), abundance,distribution, size, and behavior of underwater objects (e.g., apopulation of fish and feed). Such acoustic data measurements may, forexample, measure intensity of acoustic reflectivity of ensonifiedobjects(s) (e.g., fish) within the water to be used as an approximationof biomass. In various embodiments, such first parameter data sets maybe processed by the first biomass attribute parameter module 710 toformat or package the data set for use by, for example, biomassestimation models.

In various embodiments, the second biomass attribute parameter module712 generally represents executable instructions configured to receive asecond biomass attribute parameter data set associated with a secondbiomass attribute parameter. With reference to FIGS. 1-6 and 8-9, invarious embodiments, the second biomass attribute parameter module 712receives sensor data including the second biomass attribute parameterdata set via a wireless or wired communications link for storage,further processing, and/or distribution to other modules of the system700. For example, in the context of FIG. 2, the sensor system 202communicates at least the second parameter data set 208 b includingunder water image data corresponding to for example, the presence (orabsence), abundance, distribution, size, and behavior of underwaterobjects. In the context of FIG. 5, the sensor system 502 communicates atleast the second parameter data sets 508 b including image data from thewater surface or above the water surface corresponding to, for example,the presence (or absence), abundance, distribution, size, and behaviorof objects (e.g., a population of fish 512 as illustrated in FIG. 5).Such image data include, for example, measurements related to one ormore biomass attributes of individual fish (or with respect to two ormore fish).

In various embodiments, the first biomass estimation module 714generally represents executable instructions configured to receive thefirst biomass attribute parameter data set from the first biomassattribute parameter module 710 and generate a biomass estimate. Withreference to FIGS. 1-6 and 8-9, in various embodiments, the firstbiomass estimation module 714 receives one or more data sets embodyingparameters related to biomass, including biomass attribute parametersthat provide biomass-related body measurements, factors that mayinfluence the accuracy of biomass estimation models, and the like. Forexample, in the context of FIG. 2, the first biomass attribute parametermodule 710 receives at least the first parameter data set 208 includingacoustic data corresponding to the presence (or absence), abundance,distribution, size, and behavior of underwater objects (e.g., apopulation of fish and feed). In the context of FIG. 5, the firstbiomass attribute parameter module 710 receives at least a firstparameter data set 508 a including under water image data correspondingto the presence (or absence), abundance, distribution, size, andbehavior of underwater objects (e.g., a population of fish and feed). Invarious embodiments, the first biomass estimation module 714 utilizesone or more learned models (not shown) to generate a first biomassestimate representing one or more biomass-related metrics (i.e., abiomass estimate), as it is influenced by the biomass attributeparameters of the first parameter data set.

In various embodiments, additional inputs such as the results of feedingaccording to the estimated biomass or biomass-related physical weights(e.g., weights at harvest, body dimension measurements during physicalhandling, such as during veterinarian treatments, and the like) may beincorporated into the learned model so that the learned model evolves tofacilitate the subsequent performance of similar biomass estimation. Invarious embodiments, the learned model includes a system represented byone or more data structures, executable instructions, or combinationsthereof, that is trained and having an internal representation modifiedor adapted based in input or experience during the training process. Oneexample of the learned model is a neural network. Other implementationsinclude parametric representations, such as coefficients for dynamicsmodels, latent or explicit embedding into metric spaces for methods likenearest neighbors, or the like.

In some embodiments, the learned model of the first biomass estimationmodule 714 is initialized through a supervised learning process so as toobtain a baseline set of knowledge regarding the operational environmentand the performance of at least certain biomass estimations by the firstbiomass estimation module 714. In other embodiments, the learned modelmay be initiated at a particular processing system (e.g., computingplatform 702) by, for example, populating the learned model with theknowledge of a learned model of other similar biomass estimation modelsoptimized for different locales, or a “default knowledge core”maintained by the computing platform 702 for distribution to eachbiomass estimation as additional sensor systems and/or parameter datasets are integrated into the systems or otherwise become available tostorage and processing.

In various embodiments, the second biomass estimation module 716generally represents executable instructions configured to receive thesecond biomass attribute parameter data set from the second biomassattribute parameter module 712 and generate a biomass estimation. Withreference to FIGS. 1-6 and 8-9, in various embodiments, the secondbiomass estimation module 716 receives one or more data sets embodyingparameters related to biomass, including factors that directly influenceappetite, factors that may influence the accuracy of biomass estimationmodels, and the like. For example, in the context of FIG. 2, the secondbiomass estimation module 716 receives at least the second parameterdata set 208 b including under water image data corresponding to, forexample, the presence (or absence), abundance, distribution, size, andbehavior of underwater objects. In the context of FIG. 5, the secondbiomass estimation module 716 receives at least the second parameterdata set 508 b including image data from the water surface or above thewater surface corresponding to, for example, the presence (or absence),abundance, distribution, size, and behavior of objects (e.g., apopulation of fish 512 as illustrated in FIG. 5). In variousembodiments, the second biomass estimation module 716 utilizes one ormore learned models (not shown) to generate a second biomass estimationrepresenting one or more biomass-related metrics (i.e., a biomassestimate), as it is influenced by the biomass attribute parameters ofthe second parameter data set.

In various embodiments, additional inputs such as the results of feedingaccording to the estimated biomass or biomass-related physical weights(e.g., weights at harvest, body dimension measurements during physicalhandling, such as during veterinarian treatments, and the like) may beincorporated into the learned model so that the learned model evolves tofacilitate the subsequent performance of similar biomass estimation. Invarious embodiments, the learned model includes a system represented byone or more data structures, executable instructions, or combinationsthereof, that is trained and having an internal representation modifiedor adapted based in input or experience during the training process. Oneexample of the learned model is a neural network. Other implementationsinclude parametric representations, such as coefficients for dynamicsmodels, latent or explicit embedding into metric spaces for methods likenearest neighbors, or the like.

In some embodiments, the learned model of the second biomass estimationmodule 716 is initialized through a supervised learning process so as toobtain a baseline set of knowledge regarding the operational environmentand the performance of at least certain biomass estimations by thesecond biomass estimation module 716. In other embodiments, the learnedmodel may be initiated at a particular processing system (e.g.,computing platform 702) by, for example, populating the learned modelwith the knowledge of a learned model of other similar biomassestimation models optimized for different locales, or a “defaultknowledge core” maintained by the computing platform 702 fordistribution to each biomass estimation as additional sensor systemsand/or parameter data sets are integrated into the systems or otherwisebecome available to storage and processing.

In various embodiments, the reference parameter module 718 generallyrepresents executable instructions configured to receive one or morereference data sets associated with a biomass attribute parameter. Withreference to FIGS. 1-6 and 8-9, in various embodiments, the referenceparameter module 718 receives sensor data including at least onereference parameter data set via a wireless or wired communications linkfor storage, further processing, and/or distribution to other modules ofthe system 700. For example, in the context of FIG. 2, the sensor system202 communicates at least the reference parameter data set 208 cincluding water turbidity measurements to the processing system 210. Inthe context of FIG. 5, the sensor system 502 communicates at least thereference parameter data sets 508 c including ambient light measurementsto the processing system 510.

In various embodiments, the dynamic weighting module 720 generallyrepresents executable instructions configured to adaptively weight afirst biomass estimate (such as the biomass estimate generated by firstbiomass estimation module 714) relative to a second biomass estimate(such as the biomass estimate generated by second biomass estimationmodule 716). With reference to FIGS. 1-6 and 8-9, in variousembodiments, the dynamic weighting module 720 receives reference datasuch as the reference parameter data sets from the reference parametermodule 718 and compares, for example, environmental conditions asrepresented by the reference parameter data sets to determine therelative accuracy amongst a plurality of biomass estimation models undersuch environmental conditions. For example, in the context of FIGS. 2and 3, the processing system 210 assigns a relative weighting with thefirst weighting factor w₁ of 0.4 for the first biomass estimation model(based on acoustic data) and the second weighting factor w₂ of 0.6 forthe second biomass estimation model (based on image data) due to a firstset of environmental conditions (e.g., using environmental data from theenvironmental sensors to measure conditions for a current time or toforecast for a future time period) in which the weather is sunny, watersare clean, but waves are choppy. However, the processing system 210assigns a relative weighting with the first weighting factor w₁ of 0.9for the first biomass estimation model (based on acoustic data) and thesecond weighting factor w₂ of 0.1 for the second biomass estimationmodel (based on image data) due to the second set of environmentalconditions and discounts the image-based, second biomass estimationmodel that is expected to be less accurate in murky waters.

In various embodiments, the aggregated biomass estimate module 722generally represents executable instructions configured to determine anaggregated biomass estimate based on a combination of the first biomassestimation model using the first weight factor and the second biomassestimation model using the second weight factor. With reference to FIGS.1-6 and 8-9, in various embodiments, the aggregated biomass estimatemodule 722 receives at least the first biomass estimate generated by thefirst biomass estimation module 714, the second biomass estimategenerated by the second biomass estimation module 716, and the weightingfactors assigned by the dynamic weighting module 720. In someembodiments, such as discussed in the context of FIGS. 2, 3 and 8, theaggregated biomass estimate module 722 normalizes a plurality of biomassestimates (e.g., biomass estimates 224 a, 224 b as represented by modelbiomass estimates 302 a-312N in FIGS. 2-3) and/or biomass-relateddescriptors (e.g., biomass estimation descriptors 802 a-802 e of FIG. 8)into a common unit scale.

Subsequently, the aggregated biomass estimate module 722 applies, in thecontext of FIGS. 2-3, the assigned weighting factors w₁, w₂ to the firstnormalized model biomass estimate 304 a and the second normalized modelbiomass estimate 304 b, respectively, to generate a first weighted modelbiomass estimate 306 a and a second weighted model biomass estimate 306b. Further, the aggregated biomass estimate module 722 combines thesetwo weighted model biomass estimates 306 a, 306 b to generate aweighted, aggregated biomass estimate 308 and thereby integrates datafrom multi-sensor systems to provide a biomass estimate. In someembodiments, such as in the context of FIG. 8, the aggregated biomassestimate module 722 also generates a biomass triggered instructionsignal 810 based on the aggregated biomass estimate that instructs auser and/or farming system machinery (e.g., automated feeding system)regarding specific actions to be taken in accordance to the biomassestimate.

The system 700 also includes an electronic storage 726 includingnon-transitory storage media that electronically stores information. Theelectronic storage media of electronic storage 726 may include one orboth of system storage that is provided integrally (i.e., substantiallynon-removable) with computing platform(s) 702 and/or removable storagethat is removably connectable to computing platform(s) 702 via, forexample, a port (e.g., a USB port, a firewire port, etc.) or a drive(e.g., a disk drive, etc.). Electronic storage 726 may include one ormore of optically readable storage media (e.g., optical disks, etc.),magnetically readable storage media (e.g., magnetic tape, magnetic harddrive, floppy drive, etc.), electrical charge-based storage media (e.g.,EEPROM, RAM, etc.), solid-state storage media (e.g., flash drive, etc.),and/or other electronically readable storage media. Electronic storage726 may include one or more virtual storage resources (e.g., cloudstorage, a virtual private network, and/or other virtual storageresources). Electronic storage 726 may store software algorithms,information determined by processor(s) 728, information received fromcomputing platform(s) 702, information received from remote platform(s)704, and/or other information that enables computing platform(s) 702 tofunction as described herein.

Processor(s) 728 may be configured to provide information processingcapabilities in computing platform(s) 702. As such, processor(s) 728 mayinclude one or more of a digital processor, an analog processor, adigital circuit designed to process information, an analog circuitdesigned to process information, a state machine, and/or othermechanisms for electronically processing information. Althoughprocessor(s) 728 is shown in FIG. 7 as a single entity, this is forillustrative purposes only. In some implementations, processor(s) 728may include a plurality of processing units. These processing units maybe physically located within the same device, or processor(s) 728 mayrepresent processing functionality of a plurality of devices operatingin coordination. Processor(s) 728 may be configured to execute modules710, 712, 714, 716, 718, 720, and/or 722, and/or other modules.Processor(s) 728 may be configured to execute modules 710, 712, 714,716, 718, 720, and/or 722, and/or other modules by software; hardware;firmware; some combination of software, hardware, and/or firmware;and/or other mechanisms for configuring processing capabilities onprocessor(s) 728. As used herein, the term “module” may refer to anycomponent or set of components that perform the functionality attributedto the module. This may include one or more physical processors duringexecution of processor readable instructions, the processor readableinstructions, circuitry, hardware, storage media, or any othercomponents.

It should be appreciated that although modules 710, 712, 714, 716, 718,720, and/or 722 are illustrated in FIG. 7 as being implemented within asingle processing unit, in implementations in which processor(s) 728includes multiple processing units, one or more of modules 710, 712,714, 716, 718, 720, and/or 722 may be implemented remotely from theother modules. The description of the functionality provided by thedifferent modules 710, 712, 714, 716, 718, 720, and/or 722 describedbelow is for illustrative purposes, and is not intended to be limiting,as any of modules 710, 712, 714, 716, 718, 720, and/or 722 may providemore or less functionality than is described. For example, one or moreof modules 710, 712, 714, 716, 718, 720, and/or 722 may be eliminated,and some or all of its functionality may be provided by other ones ofmodules 710, 712, 714, 716, 718, 720, and/or 722. As another example,processor(s) 728 may be configured to execute one or more additionalmodules that may perform some or all of the functionality attributedbelow to one of modules 710, 712, 714, 716, 718, 720, and/or 722.

Referring now to FIG. 8, illustrated is another example diagram 800 ofadaptive weighting of biomass estimations in accordance with someembodiments. As illustrated, a plurality of biomass estimates 802 (whichmay be model-based outputs or otherwise) include a first biomassestimation descriptor 802 a, a second biomass estimation descriptor 802b, a third biomass estimation descriptor 802 c, a fourth biomassestimation descriptor 802 d, and a fifth biomass estimation descriptor802 e.

The first biomass estimation descriptor 802 a, as represented by theword ‘adult’, corresponds to a textual description indicating size orlifecycle status of fish within a marine enclosure under observation(e.g., fry vs. smolt vs. adult). In various embodiments, the firstbiomass estimation descriptor 802 a may be determined based on, forexample, data measured by any of the sensor systems discussed herein,such as acoustics-data based sensor system 202 a of FIG. 2 or byhuman-vision-perception from a video image stream captured by theimage-data based sensor system 202 b of FIG. 2. The first biomassestimation descriptor 802 a may be indicative of an overall sizeestimate of fish, feed formulations and amounts to be dispensed based onlife cycle, and the like.

The second biomass estimation descriptor 802 b, as represented by thenumber 9, corresponds to a human-provided value on an integer scale of0-10. In various embodiments, the second biomass estimation descriptor802 b may be determined via human visual perception and based onoperator personal experience. For example, in some embodiments, thenumerical value 9 may represent that the operator visually estimatesfish size to be close to harvest weight, which is indicative ofincreased biomass levels. The third biomass estimation descriptor 802 c,as represented by the color green, corresponds to a color-codeddescriptor relative to average target weight of animals in a red,yellow, and green coloring scheme in which green is indicative ofanimals being within an expected range of weight (e.g., not underweight,starving, and the like).

The fourth biomass estimation descriptor 802 d, as represented by thepercentage value 80%, corresponds to a percentage quantification oftotal biomass (e.g., individuals in a population) that have reached athreshold size for harvesting. As a hypothetical example, in someembodiments, a predetermined threshold is set such weight of individualfish exceeding 4 kilograms are ready to be harvested. The fifth biomassestimation descriptor 802 e, as represented by the value 0.4,corresponds to feeding rate instructions as it relates to pellets perfish individual per minute (PPFPM). In various embodiments, this 0.4PPFPM value is indicative of a level of appetite at which fish of theestimated biomass level is expected to eat.

As is evident, each of the plurality of biomass estimations 802 is adescriptor that is related to biomass in some manner. However, it willbe appreciated that the descriptors and values associated with theplurality of biomass estimations 802 do not share a common baseline forcomparison. Accordingly, in various embodiments, the systems describedherein (e.g., systems 100-700 of FIGS. 1-7) normalizes each of theplurality of biomass estimations 802 based on a comparison to acorresponding baseline or predetermined threshold, respectively, togenerate a normalized model biomass estimate 804. That is, the systemsare configured to normalize each of the biomass estimations to a commonbiomass estimate scale based on their respective comparisons relative tosome established parameter, threshold, expected baseline, and the likeon which the individual biomass estimation descriptors are based.

For example, with respect to the first biomass estimation, the systemsdescribed herein normalize the first biomass estimation descriptor 802 abased on a comparison to life cycle stage with respect to biomasslocation to generate a first normalized model biomass estimate 804 a of75 based on an example one hundred point scale. Similarly, with respectto the second biomass estimation, the systems normalize the secondbiomass estimation descriptor 802 b based on a comparison of thehuman-provided value of 9 to the integer scale of 0-10 on which it isbased for conversion to the same one hundred point scale and generate asecond normalized model biomass estimate 804 b of 90.

With respect to the third biomass estimation, the systems normalize thethird biomass estimation descriptor 802 c of the green color based on acomparison to a color-coded descriptor of hunger in a red, yellow, andgreen coloring scheme in which green is indicative of biomass at ontarget weight levels to generate a third normalized model biomassestimate 804 c of 70 in the one hundred point common scale. With respectto the fourth biomass estimation, the systems normalize the fourthappetite descriptor 802 d of the percentage 80% based on a comparisonrelative to an expected percentage quantification of total biomass(e.g., individuals in a population) that are forecasted to have reacheda threshold size for harvesting. In this illustrative example, 80% ofbiomass being above the threshold level generates a fourth normalizedmodel biomass estimate 804 d of 55 in the one hundred point commonscale. Lastly, with respect to the fifth biomass estimation, the systemsnormalize the fifth appetite descriptor 802 e of 0.4 PPFPM based on acomparison to feed rates to generate a fifth normalized model biomassestimate 804 e of 50 in the one hundred point common scale.

It should be recognized that the above-mentioned bases for normalizationof disparate biomass estimation descriptors having different individualunderlying scales (and also the one hundred point normalization scale)are provided only for simplified illustrative purposes only. In variousembodiments, the systems described herein may utilize any appropriatebasis for conversion of the different appetite descriptors 802 a-802 eto a common normalization scale as will be understood by those skilledin the art.

The systems will adaptively weight the first through fifth biomassestimation models and their associated appetite descriptors/normalizedbiomass estimate values in a manner similar to that previously discussedin more detail relative to FIGS. 1-7. For ease of illustration, theweighting has been shown in FIG. 8 to be equal weighted such that thesystems assign a first weighting factor w₁ of 0.2 to the first biomassestimation model, a second weighting factor w₂ of 0.2 to the secondbiomass estimation model, a third weighting factor w₃ of 0.2 to thethird biomass estimation model, a fourth weighting factor w₄ of 0.2 tothe fourth biomass estimation model, and a fifth weighting factor w₅ of0.2 to the fifth biomass estimation model. Subsequently, the systemsapplies the assigned weighting factors w₁-w₅ to the first through fifthnormalized model biomass estimates 804 a-804 e, respectively to generatea first weighted model biomass estimate 806 a of 15, a second weightedmodel biomass estimate 806 b of 18, a third weighted model biomassestimate 806 c of 14, a fourth weighted model biomass estimate 806 d of11, and a fifth weighted model biomass estimate 806 e of 10. Further,the system combines these five weighted model biomass estimates 806a-806 e to generate a weighted, aggregated biomass estimate 808 of 68and thereby integrates data from multi-sensor systems to provide aconsensus biomass estimate incorporating biomass estimates from multiplesources.

In various embodiments, the aggregated biomass estimate 808 may bedisplayed in a graphical user interface for presentation to a user. Forexample, a value of 68 (within a 100 point scale) for the aggregatedbiomass estimate 808 provides context regarding expected biomassquantities, and the user may take action accordingly. In otherembodiments, the systems may optionally generate a biomass triggeredinstruction signal 810 (as represented by the dotted line box) thatinstructs a user and/or automated feeding system regarding specificactions to be taken in accordance to the biomass estimate (e.g., asquantified by the aggregated biomass estimate 808).

Similar to the first through fifth biomass estimations discussed herewith respect to FIG. 8, it will be appreciated that the biomasstriggered instruction signal 810 is not limited to any particular formatand in various embodiments may, in a manner similar to a reversing ofthe previous normalization operations, be converted to any appropriateformat. Such formats for the biomass triggered instruction signal 810include, by way of non-limiting example, a stop signal, a color-codeduser interface display, a specific feed rate that should beadministered, a total feed volume that should be administered, and thelike. Additionally, although described above in the context offeeding-related signals, those skilled in the art will recognize thatbiomass triggered instruction signals are not limited to such contextsand includes any response that may be initiated based at least in parton biomass information such as feed optimization (e.g., changing from astarter feed to a finishing feed, changing feeding rates), harvestactions (e.g., scheduling harvest or pickup of a portion or an entiretyof an animal population), animal transfer actions (e.g., transferringfrom one environment or enclosure to another, sorting animals intodifferent enclosures, culling a subset of the animal population),intervention actions (e.g., scheduling veterinarian visit due tounexpected stall or drop in growth rates), and the like.

It should be recognized that although biomass attributes and aggregatedbiomass estimation has been primarily discussed here in the context ofdynamic weighting of various biomass estimation model outputs, thoseskilled in the art will recognize that biomass attribute parameters alsohave variable contribution with respect to multi-parameter biomassestimation models. Referring now to FIG. 9, illustrated is an examplediagram of adaptive weighting of biomass attribute parameters formulti-parameter biomass estimation models in accordance with someembodiments.

As illustrated, a plurality of biomass estimation models (e.g., thefirst biomass estimation model 922 a and the second biomass estimationmodel 922 b) receive their respective inputs (e.g., one or more of thebiomass attribute parameter data sets 900) and generate a plurality ofbiomass estimations for a number N of different biomass estimationmodels. In various embodiments, the first biomass estimation model 922 ais a volumetric model for estimating biomass of an animal based on, forexample, body volume and body weight correlations. The first biomassestimation model 922 a receives, as input, various biomass attributeparameter data sets 900 including a first biomass attribute parameterdata set 900 a and a second biomass attribute parameter data set 900 b.

In some embodiments, the first biomass attribute parameter data set 900a includes volumetric rendering data representing body volume of ananimal in three-dimensional space. In various embodiments, suchvolumetric rendering data may include a three-dimensional point clouddata set representing the surface of an animal. Those skilled in the artwill recognize that body surface data may be generated by varioussensors (including many sensors described throughout this disclosure)including but not limited to laser scanners, structured light sensors,multi-perspective image rendering, ultrasound imaging, magneticresonance imaging, and the like.

Body conformation and composition is different amongst different breedsof animals (e.g., breast width in chickens), and different coefficientsand exponents are needed for different animal breeds. Accordingly, bodycomposition data is often desirable not only for improved body weightestimation but also for producers looking for insight regardingparticular meat qualities (e.g., leaner meat). As will be appreciated,lean muscle mass weighs differently and takes up a different amount ofvolumetric space relative to body fat per unit weight. Various factorssuch as body composition of an animal will alter its conductivity inelectromagnetic fields. Body fat is generally a poor conductor, whereascomponents of lean body mass (including water and electrolytes) aregenerally good conductors. In some embodiments, the second biomassattribute parameter data set 900 b includes body composition data suchas lean body mass, skeletal mass, and the like. Those skilled in the artwill recognize that body composition data may be generated by varioussensors (including many sensors described throughout this disclosure)including but not limited to ultrasonic systems to estimate fat contentof particular body parts, total body electrical conductivity sensors,near infrared interactance sensors, x-ray absorptiometry sensors,acoustic sensors, and the like.

In various embodiments, the first biomass estimation model 922 areceives a plurality of biomass attribute parameter data sets (such asthe volumetric data, body composition data, and the like) and generatesone or more output biomass estimate values such as a first model biomassestimate 902 a (e.g., body weight). As will be appreciated by thoseskilled in the art, volume renderings and three dimensional volumetricmay have variable precision or accuracy for biomass estimation in thepresence (and/or absence) of other factors such as body composition dataof the second biomass attribute parameter data set 900 b, density data,elasticity data, acoustic impedance data, and the like.

At a high level of abstraction for providing an example, a processingsystem (e.g., processing system 110 of FIG. 1) assigns a first weightingfactor w₁ to the first biomass attribute parameter data set 900 a and asecond weighting factor w₂ to the second biomass attribute parameterdata set 900 b when estimating the biomass of a sheared sheep. However,body surface scanning and its resultant body volume rendering would beless reliable when using, for example, 3D laser scanners to generatepoint cloud data for the same sheep that has not had its wool clipped.Accordingly, in such circumstances, the processing system assigns adifferent weighting factor to the first biomass attribute parameter dataset 900 a that is less than the first weighting factor w₁ of FIG. 9 dueto occlusion in imagery or sensor data occlusion (e.g., the non-animalobject of hair in this example hypothetical that reduces accuracy ofbody surface scanning).

In some embodiments, the second biomass estimation model 922 b is anallometric model that extrapolates one or more measures of body partsize to overall body size (e.g., weight). Allometric relationships arefairly well defined for a number of animal species, allowing themeasurement of one or more parts of an animal to enable accurateestimates of other parameters (e.g., relating a body part measurement tototal body weight of an animal). For example, body length and weight arehighly correlated in many fish species. Similarly, certain bodymeasurements, such as heart girth in swine and shank length in poultry,are also known to be proportional to total body weight. In variousembodiments, such body part size data includes animal body measurementsincluding but not limited to measurements of length, width, height,girth, curvature, other dimensional features of various body parts, andother geometrical features relevant to biomass estimation. Those skilledin the art will recognize that body part size data may be generated byvarious sensors including many sensors described throughout thisdisclosure.

The second biomass estimation model 922 b receives, as input, variousbiomass attribute parameter data sets 900 including the second biomassattribute parameter data set 900 b (which was also used by the firstbiomass estimation model 922 a) and a third biomass attribute parameterdata set 900 c. In some embodiments, the third biomass attributeparameter data set 900 c includes a heart girth measurement. The secondbiomass estimation model 922 receives a plurality of biomass attributeparameter data sets (such as the body composition data, the heart girthmeasurement, and the like) and generates one or more output biomassestimate values such as a second model biomass estimate 902 b (e.g.,body weight).

At a high level of abstraction for providing an example, a processingsystem (e.g., processing system 110 of FIG. 1) assigns a third weightingfactor w₃ to the second biomass attribute parameter data set 900 b and afourth weighting factor w₄ to the third biomass attribute parameter dataset 900 c when estimating the biomass of a pig using heart girthmeasurements generated from imagery from multiple perspectives (e.g.,including two or more of a left side view, a right side view, a top-downview, a perspective view, a bottom-up view, and the like). As will beappreciated by those skilled in the art, biomass estimation usingallometric relationships may have variable precision or accuracy in thepresence (and/or absence) of other factors such as body composition dataof the second biomass attribute parameter data set 900 b, availabilityof other body part measurements, the accuracy of body part measurements,and the like. Accordingly, for a hypothetical circumstance in whichheart girth is generated only a singular perspective image view in whichpart of the pig is occluded by other objects (e.g., other pigs), theprocessing system assigns a different weighting factor to the thirdbiomass attribute parameter data set 900 c that is less than the fourthweighting factor w₄ of FIG. 9.

In this manner, the dynamic weighting of biomass attribute data setsdescribed herein accounts for variable contribution of different datasets in estimating biomass. Using allometric relationships, the dynamicweighting of biomass attributes allows for assessment of variousimportant biological parameters in the field including body part sizerelative to growth or size of entire animal bodies. The dynamicmulti-parameter weighting of FIG. 9 described above generates variousbiomass estimates 902 that, in various embodiments, are utilizable bythemselves to generate biomass-triggered instruction signals (notshown). In some embodiments, a multi-parameter biomass estimate (e.g.,first model biomass estimate 902 a) is further dynamically weighted incombination with additional biomass estimates (e.g., second modelbiomass estimate 902 b, biomass estimates 302 of FIG. 3, and the like)to generate a consensus biomass estimate (e.g., aggregated biomassestimate 908).

In a manner similar to the operations previously described with respectto FIG. 3, in various embodiments, a processing system optionally (asindicated by the dotted lines) normalizes each of the model biomassestimates 902 a through 902N to a common biomass representation scale.For example, as illustrated in FIG. 9, the processing system normalizesthe first model biomass estimate 902 a to generate a first normalizedmodel biomass estimate 904 a. Similarly, the processing systemnormalizes the second model biomass estimate 902 b based on the samescale to generate a second normalized model biomass estimate 904 b.

In various embodiments, such as previously discussed in more detail withrespect to FIG. 2, environmental sensors generate environmental datathat serves as reference data for implementing the dynamic weighting ofvarious estimates from a plurality of biomass estimation models. Forexample, in various embodiments, the biomass attribute parameter datasets 900 are compared relative to measured reference (e.g.,environmental) data. Accordingly, the processing system assignsweighting factors w₁ to the first biomass estimate 902 a (or thenormalized estimate), w_(J) to the second biomass estimate 902 b (or thenormalized estimate), and w_(K) to the Nth biomass estimate 902N (or thenormalized estimate) to generate weighted model biomass estimates 906 a,906 b, through 906N, respectively.

Further, the processing system combines these weighted biomass estimates906 a-906N to generate a weighted, aggregated biomass estimate 908 andthereby integrates data from multi-sensor systems (some sensors andestimation models being more accurate than others, sometimes in allinstances or sometimes depending on variable factors such as theenvironment) measuring biomass attributes to provide a biomass estimate.Similarly, although not explicitly described herein, the weighted,aggregated biomass estimate 908 may be generated by integrating biomassattribute data and estimates from multiple discrete body-part-to-biomassmodels (e.g., poultry shank-length and head-size to whole body weight).

In various embodiments, the systems described herein may optionallygenerate a biomass triggered instruction signal 910 (as represented bythe dotted line box) based at least in part on the aggregated biomassestimate 908 that instructs a user and/or one or more farm systemsregarding specific actions to be taken in accordance to the biomassestimate (e.g., as quantified by the aggregated biomass estimate 908).For example, in some embodiments, the biomass triggered instructionsignal 910 (as represented by the dotted line box) based at least inpart on the aggregated biomass estimate 908 that instructs an automatedfeeding system regarding specific actions to be taken in accordance tothe biomass estimate (e.g., change an amount of feed dispensed, change aformulation of feed dispensed, and the like).

Although primarily discussed here in the context of aquaculture as itrelates to the feeding of fish, those skilled in the art will recognizethat the techniques described herein may be applied to any aquatic,aquaculture species such as shellfish, crustaceans, bivalves, and thelike without departing from the scope of this disclosure. Further, thoseskilled in the art will recognize that the techniques described hereinmay also be applied to adaptively weighting feeding models and providingaggregated biomass estimation for any husbandry animal that is reared inan environment in which multiple different sensor systems are applied,and for which the sensor systems will vary in accuracy of biomassestimation depending on environmental conditions, differing availabilityof data over time, differing data granularities, and the like.

Additionally, although primarily illustrated and discussed here in thecontext of fish being positioned in an open water environment (whichwill also include an enclosure of some kind to prevent escape of fishinto the open ocean), those skilled in the art will recognize that thetechniques described herein may similarly be applied to any type ofaquatic farming environment. For example, such aquatic farmingenvironments may include, by way of non-limiting example, lakes, ponds,open seas, recirculation aquaculture systems (RAS) to provide for closedsystems, raceways, indoor tanks, outdoor tanks, and the like.Additionally, those skilled in the art will recognize that an enclosureincludes any device or method used to contain an animal.

Accordingly, as discussed herein, FIGS. 1-9 describe techniques thatimprove the precision and accuracy of biomass estimation and decreasingthe uncertainties associated with conventional biomass estimationsystems by adaptively weighting different biomass attributes and biomassestimates from different biomass estimation models and combining theminto an aggregated biomass estimate that is more accurate than would beindividually provided by each biomass estimation model by itself.Further, the techniques described here provide an efficient manner forfarmers to integrate the ever increasing suite of available sensortechnologies in the future with any sensors currently utilized at theirfarming sites to improve biomass estimation and the results ofaquaculture operations. Additionally, the techniques described hereprovide for improved estimation of an animal's current weight or, usingserial measurements, can be used to monitor trends in weight gain orloss (e.g., to allow earlier identification of conditions resulting inweight problems such as loss of appetite/feeding due to disease).

In some embodiments, certain aspects of the techniques described abovemay implemented by one or more processors of a processing systemexecuting software. The software includes one or more sets of executableinstructions stored or otherwise tangibly embodied on a non-transitorycomputer readable storage medium. A computer readable storage medium mayinclude any non-transitory storage medium, or combination ofnon-transitory storage media, accessible by a computer system during useto provide instructions and/or data to the computer system. Such storagemedia can include, but is not limited to, optical media (e.g., compactdisc (CD), digital versatile disc (DVD), Blu-Ray disc), magnetic media(e.g., floppy disc, magnetic tape, or magnetic hard drive), volatilememory (e.g., random access memory (RAM) or cache), non-volatile memory(e.g., read-only memory (ROM) or Flash memory), ormicroelectromechanical systems (MEMS)-based storage media. The computerreadable storage medium may be embedded in the computing system (e.g.,system RAM or ROM), fixedly attached to the computing system (e.g., amagnetic hard drive), removably attached to the computing system (e.g.,an optical disc or Universal Serial Bus (USB)-based Flash memory), orcoupled to the computer system via a wired or wireless network (e.g.,network accessible storage (NAS)).

The software can include the instructions and certain data that, whenexecuted by the one or more processors, manipulate the one or moreprocessors to perform one or more aspects of the techniques describedabove. The non-transitory computer readable storage medium can include,for example, a magnetic or optical disk storage device, solid statestorage devices such as Flash memory, a cache, random access memory(RAM) or other non-volatile memory device or devices, and the like. Theexecutable instructions stored on the non-transitory computer readablestorage medium may be in source code, assembly language code, objectcode, or other instruction format that is interpreted or otherwiseexecutable by one or more processors.

Note that not all of the activities or elements described above in thegeneral description are required, that a portion of a specific activityor device may not be required, and that one or more further activitiesmay be performed, or elements included, in addition to those described.Still further, the order in which activities are listed are notnecessarily the order in which they are performed. Also, the conceptshave been described with reference to specific embodiments. However, oneof ordinary skill in the art appreciates that various modifications andchanges can be made without departing from the scope of the presentdisclosure as set forth in the claims below. Accordingly, thespecification and figures are to be regarded in an illustrative ratherthan a restrictive sense, and all such modifications are intended to beincluded within the scope of the present disclosure.

Benefits, other advantages, and solutions to problems have beendescribed above with regard to specific embodiments. However, thebenefits, advantages, solutions to problems, and any feature(s) that maycause any benefit, advantage, or solution to occur or become morepronounced are not to be construed as a critical, required, or essentialfeature of any or all the claims. Moreover, the particular embodimentsdisclosed above are illustrative only, as the disclosed subject mattermay be modified and practiced in different but equivalent mannersapparent to those skilled in the art having the benefit of the teachingsherein. No limitations are intended to the details of construction ordesign herein shown, other than as described in the claims below. It istherefore evident that the particular embodiments disclosed above may bealtered or modified and all such variations are considered within thescope of the disclosed subject matter. Accordingly, the protectionsought herein is as set forth in the claims below.

What is claimed is:
 1. A method for providing an animal biomass estimate, the method comprising: providing a first biomass parameter data set associated with a first biomass attribute parameter to a first biomass estimation model; providing a second biomass parameter data set associated with a second biomass attribute parameter to a second biomass estimation model different from the first biomass estimation model; adaptively weighting the first biomass estimation model with a first weighting factor relative to a second weighting factor for the second biomass estimation model; and determining an aggregated biomass estimate based on a combination of the first biomass estimation model using the first weight factor and the second biomass estimation model using the second weight factor.
 2. The method of claim 1, adaptively weighting the first biomass estimation model with the first weighting factor comprises: adaptively weighting the first biomass attribute parameter with the first weighting factor relative to the second weighting factor for the second biomass attribute parameter.
 3. The method of claim 1, wherein adaptively weighting the first biomass estimation model with the first weighting factor relative to the second weighting factor for the second biomass estimation model further comprises: providing a measured reference data related to the first biomass attribute parameter and the second biomass attribute parameter; assigning the first weight factor to the first biomass estimation model based on a comparison of the first biomass parameter data set with the measured reference data; and assigning the second weight factor to the second biomass estimation model based on a comparison of the second biomass parameter data set with the measured reference data.
 4. The method of claim 3, further comprising: providing measured reference data that is indicative of one or more of a relative accuracy difference, a relative data availability difference, and a relative data granularity difference between the first biomass parameter data set and the second biomass parameter data set.
 5. The method of claim 3, further comprising: providing measured environmental data that is indicative of one or more environmental conditions associated with capture of the first biomass parameter data set and the second biomass parameter data set.
 6. The method of claim 1, further comprising: generating, based at least in part on the aggregated biomass estimate, a feeding recommendation related to administration of feed.
 7. The method of claim 6, further comprising: providing a feeding instruction signal based on one or more of the feeding recommendation and the aggregated biomass estimate for modifying operations of a feed control system.
 8. A non-transitory computer readable medium embodying a set of executable instructions, the set of executable instructions to manipulate at least one processor to: provide a first biomass parameter data set associated with a first biomass attribute parameter to a first biomass estimation model; provide a second biomass parameter data set associated with a second biomass attribute parameter to a second biomass estimation model different from the first biomass estimation model; adaptively weight the first biomass estimation model with a first weighting factor relative to a second weighting factor for the second biomass estimation model; and determine an aggregated biomass estimation based on a combination of the first biomass estimation model using the first weight factor and the second biomass estimation model using the second weight factor.
 9. The non-transitory computer readable medium of claim 8, further embodying executable instructions to manipulate at least one processor to: adaptively weight the first biomass attribute parameter with the first weighting factor relative to the second weighting factor for the second biomass attribute parameter.
 10. The non-transitory computer readable medium of claim 8, further embodying executable instructions to manipulate at least one processor to: provide a measured reference data related to the first biomass attribute parameter and the second biomass attribute parameter; assign the first weight factor to the first biomass estimation model based on a comparison of the first biomass parameter data set with the measured reference data; and assign the second weight factor to the second biomass estimation model based on a comparison of the second biomass parameter data set with the measured reference data.
 11. The non-transitory computer readable medium of claim 10, further embodying executable instructions to manipulate at least one processor to: provide measured reference data that is indicative of one or more of a relative accuracy difference, a relative data availability difference, and a relative data granularity difference between the first biomass parameter data set and the second biomass parameter data set.
 12. The non-transitory computer readable medium of claim 10, further embodying executable instructions to manipulate at least one processor to: provide measured environmental data that is indicative of one or more environmental conditions associated with capture of the first biomass parameter data set and the second biomass parameter data set.
 13. The non-transitory computer readable medium of claim 8, further embodying executable instructions to manipulate at least one processor to: generate, based at least in part on the aggregated biomass estimate, a feeding recommendation related to administration of feed.
 14. The non-transitory computer readable medium of claim 13, further embodying executable instructions to manipulate at least one processor to: provide a feeding instruction signal based on one or more of the feeding recommendation and the aggregated biomass estimate for modifying operations of a feed control system.
 15. A system, comprising: a first set of one or more sensors configured to generate a first biomass parameter data set associated with a first biomass attribute parameter; a second set of one or more sensors configured to generate a second biomass parameter data set associated with a second biomass attribute parameter; a digital storage medium, encoding instructions executable by a computing device; a processor, communicably coupled to the digital storage medium, configured to execute the instructions, wherein the instructions are configured to: provide the first biomass parameter data set to a first biomass estimation model; provide the second biomass parameter data set to a second biomass estimation model different from the first biomass estimation model; adaptively weight the first biomass estimation model with a first weighting factor relative to a second weighting factor for the second biomass estimation model; and determine an aggregated biomass estimate based on a combination of the first biomass estimation model using the first weight factor and the second biomass estimation model using the second weight factor.
 16. The system of claim 15, wherein the processor is further configured to: receive a measured reference data related to the first biomass attribute parameter and the second biomass attribute parameter; assign the first weight factor to the first biomass estimation model based on a comparison of the first biomass parameter data set with the measured reference data; and assign the second weight factor to the second biomass estimation model based on a comparison of the second biomass parameter data set with the measured reference data.
 17. The system of claim 16, wherein the processor is further configured to: receive measured reference data that is indicative of one or more of a relative accuracy difference, a relative data availability difference, and a relative data granularity difference between the first biomass parameter data set and the second biomass parameter data set.
 18. The system of claim 16, wherein the processor is further configured to: receive measured environmental data that is indicative of one or more environmental conditions associated with capture of the first biomass parameter data set and the second biomass parameter data set.
 19. The system of claim 15, wherein the first set of one or more sensors includes one or more cameras configured to capture imagery of animals within an aquatic environment.
 20. The system of claim 15, wherein the second set of one or more sensors includes one or more acoustic sensors configured to record acoustic data generated by animals within an aquatic environment. 